# NCSS Statistical Software

Size: px
Start display at page: ## Transcription

1 Chapter 06 Introduction This procedure provides several reports for the comparison of two distributions, including confidence intervals for the difference in means, two-sample t-tests, the z-test, the randomization test, the Mann-Whitney U nonparametric test, and the Kolmogorov-Smirnov test. Tests of assumptions and plots are also available in this procedure. The data for this procedure can be contained in two variables (columns) or in one variable indexed by a second (grouping) variable. Research Questions A common task in research is to compare two populations or groups. Researchers may wish to compare the income level of two regions, the nitrogen content of two lakes, or the effectiveness of two drugs. An initial question that arises is what aspects (parameters) of the populations should be compared. We might consider comparing the averages, the medians, the standard deviations, the distributional shapes, or maximum values. The comparison parameter is based on the particular problem. The typical comparison of two distributions is the comparison of means. If we can safely make the assumption of the data in each group following a normal distribution, we can use a two-sample t-test to compare the means of random samples drawn from these two populations. If these assumptions are severely violated, the nonparametric Mann-Whitney U test, the randomization test, or the Kolmogorov-Smirnov test may be considered instead. Technical Details The technical details and formulas for the methods of this procedure are presented in line with the Example 1 output. The output and technical details are presented in the following order: Descriptive Statistics and Confidence Intervals of Each Group Confidence Interval of μμ 1 μμ Bootstrap Confidence Intervals Equal-Variance T-Test and associated power report Aspin-Welch Unequal-Variance T-Test and associated power report Z-Test Randomization Test Mann-Whitney Test Kolmogorov-Smirnov Test Tests of Assumptions Graphs 06-1

2 Data Structure The data may be entered in two formats, as shown in the two examples below. The examples give the yield of corn for two types of fertilizer. The first format is shown in the first table in which the responses for each group are entered in separate variables. That is, each variable contains all responses for a single group. In the second format the data are arranged so that all responses are entered in a single variable. A second variable, the Grouping Variable, contains an index that gives the group (A or B) to which the row of data belongs. In most cases, the second format is more flexible. Unless there is some special reason to use the first format, we recommend that you use the second. Two Response Variables Yield A Yield B Grouping and Response Variables Fertilizer Yield B 546 B 547 B 774 B 465 B 459 B 665 B A 45 A 874 A 554 A 447 A 356 A 754 A 558 A 574 A

3 Null and Alternative Hypotheses For comparing two means, the basic null hypothesis is that the means are equal, with three common alternative hypotheses, HH 0 : μμ 1 = μμ HH aa : μμ 1 μμ, HH aa : μμ 1 < μμ, or HH aa : μμ 1 > μμ, one of which is chosen according to the nature of the experiment or study. A slightly different set of null and alternative hypotheses are used if the goal of the test is to determine whether μμ 1 or μμ is greater than or less than the other by a given amount. The null hypothesis then takes on the form and the alternative hypotheses, HH 0 : μμ 1 μμ = Hypothesized Difference HH aa : μμ 1 μμ Hypothesized Difference HH aa : μμ 1 μμ < Hypothesized Difference HH aa : μμ 1 μμ > Hypothesized Difference These hypotheses are equivalent to the previous set if the Hypothesized Difference is zero. Assumptions The following assumptions are made by the statistical tests described in this section. One of the reasons for the popularity of the t-test, particularly the Aspin-Welch Unequal-Variance t-test, is its robustness in the face of assumption violation. However, if an assumption is not met even approximately, the significance levels and the power of the t-test are invalidated. Unfortunately, in practice it sometimes happens that one or more assumption is not met. Hence, take the appropriate steps to check the assumptions before you make important decisions based on these tests. There are reports in this procedure that permit you to examine the assumptions, both visually and through assumptions tests. Assumptions The assumptions of the two-sample t-test are: 1. The data are continuous (not discrete).. The data follow the normal probability distribution. 3. The variances of the two populations are equal. (If not, the Aspin-Welch Unequal-Variance test is used.) 4. The two samples are independent. There is no relationship between the individuals in one sample as compared to the other (as there is in the paired t-test). 5. Both samples are simple random samples from their respective populations. Each individual in the population has an equal probability of being selected in the sample. 06-3

4 Mann-Whitney U Test Assumptions The assumptions of the Mann-Whitney U test are: 1. The variable of interest is continuous (not discrete). The measurement scale is at least ordinal.. The probability distributions of the two populations are identical, except for location. 3. The two samples are independent. 4. Both samples are simple random samples from their respective populations. Each individual in the population has an equal probability of being selected in the sample. Kolmogorov-Smirnov Test Assumptions The assumptions of the Kolmogorov-Smirnov test are: 1. The measurement scale is at least ordinal.. The probability distributions are continuous. 3. The two samples are mutually independent. 4. Both samples are simple random samples from their respective populations. Procedure Options This section describes the options available in this procedure. To find out more about using a procedure, go to the Procedures chapter. Following is a list of the options of this procedure. Variables Tab The options on this panel specify which variables to use. Input Type In NCSS, there are three ways to organize the data for comparing two means or distributions. Select the type that reflects the way your data is presented on the spreadsheet. Response Variable(s) and Group Variable(s) In this scenario, the response data is in one column and the groups are defined in another column of the same length. Response Group If multiple response variables are chosen, a separate comparison is made for each response variable. If the group variable has more than two levels, a comparison is made among each pair of levels. If multiple group variables are entered, a separate comparison is made for each pair of each combination of group levels. 06-4

5 Two Variables with Response Data in each Variable In this selection, the data for each group is in a separate column. You are given two boxes to select the Group 1 data variable and the Group data variable. Group 1 Group More than Two Variables with Response Data in each Variable For this choice, the data for each group is in a separate column. Each variable listed in the variable box will be compared to every other variable in the box. Group 1 Group Group Variables Input Type: Response Variable(s) and Group Variable(s) Response Variable(s) Specify the variable(s) containing the response data. For this input type, the response data is in this column and the groups are defined in another column of the same length. Response Group If more than one response variable is entered, a separate analysis is produced for each variable. Group Variable(s) Specify the variable(s) defining the grouping of the response data. For this input type, the group data is in this column and the response data is in another column of the same length. Response Group If the group variable has more than two levels, a comparison is made among each pair of levels. If multiple group variables are entered, a separate comparison is made for each pair of each combination of group levels. The limit to the number of Group Variables is

6 Variables Input Type: Two Variables with Response Data in each Variable Group 1 Variable Specify the variable that contains the Group 1 data. For this input type, the data for each group is in a separate column. The number of values in each column need not be the same. Group 1 Group Group Variable Specify the variable that contains the Group data. Variables Input Type: More than Two Variables with Response Data in each Variable Response Variables Specify the variable(s) containing the response data. For this choice, the data for each group is in a separate column. Each variable listed in the variable box will be compared to every other variable in the box. Group 1 Group Group Reports Tab The options on this panel specify which reports will be included in the output. Descriptive Statistics and Confidence Intervals Confidence Level This confidence level is used for the descriptive statistics confidence intervals of each group, as well as for the confidence interval of the mean difference. Typical confidence levels are 90%, 95%, and 99%, with 95% being the most common. Descriptive Statistics and Confidence Intervals of Each Group This section reports the group name, count, mean, standard deviation, standard error, confidence interval of the mean, median, and confidence interval of the median, for each group. Confidence Interval of μμ 11 μμ This section reports the confidence interval for the difference between the two means for both the equal variance and unequal variance assumption formulas. Limits Specify whether a two-sided or one-sided confidence interval of the difference (μμ 1 μμ ) is to be reported. 06-6

7 Two-Sided For this selection, the lower and upper limits of μμ 1 μμ are reported, giving a confidence interval of the form (Lower Limit, Upper Limit). One-Sided Upper For this selection, only an upper limit of μμ 1 μμ is reported, giving a confidence interval of the form (-, Upper Limit). One-Sided Lower For this selection, only a lower limit of μμ 1 μμ is reported, giving a confidence interval of the form (Lower Limit, ). Bootstrap Confidence Intervals This section provides confidence intervals of desired levels for the difference in means, as well as for the individual means of each group. Bootstrap Confidence Levels These are the confidence levels of the bootstrap confidence intervals. All values must be between 50 and 100. You may enter several values, separated by blanks or commas. A separate confidence interval is generated for each value entered. Examples: :99(1) 90 Samples (N) This is the number of bootstrap samples used. A general rule of thumb is to use at least 100 when standard errors are the focus or 1000 when confidence intervals are your focus. With current computer speeds, 10,000 or more samples can usually be run in a short time. Retries If the results from a bootstrap sample cannot be calculated, the sample is discarded and a new sample is drawn in its place. This parameter is the number of times that a new sample is drawn before the algorithm is terminated. It is possible that the sample cannot be bootstrapped. This parameter lets you specify how many alternative bootstrap samples are considered before the algorithm gives up. The range is 1 to 1000, with 50 as a recommended value. Bootstrap Confidence Interval Method This option specifies the method used to calculate the bootstrap confidence intervals. Percentile The confidence limits are the corresponding percentiles of the bootstrap values. Reflection The confidence limits are formed by reflecting the percentile limits. If X0 is the original value of the parameter estimate and XL and XU are the percentile confidence limits, the Reflection interval is ( X0 - XU, X0 - XL). 06-7

8 Percentile Type This option specifies which of five different methods is used to calculate the percentiles. More details of these five types are offered in the Descriptive Statistics chapter. Tests Alpha Alpha is the significance level used in the hypothesis tests. A value of 0.05 is most commonly used, but 0.1, 0.05, 0.01, and other values are sometimes used. Typical values range from to 0.0. H0 Value This is the hypothesized difference between the two population means. To test only whether there is a difference between the two groups, this value is set to zero. If, for example, a researcher wished to test whether the Group 1 population mean is greater than the Group population mean by at least 5, this value would be set to 5. Alternative Hypothesis Direction Specify the direction of the alternative hypothesis. This direction applies to all tests performed in this procedure, with exception of the Randomization test, which only gives the two-sided test results. The selection 'Two-Sided and One-Sided' will produce all three tests for each test selected. Tests - Parametric Equal Variance T-Test This section reports the details of the T-Test for comparing two means when the variance of the two groups is assumed to be equal. Power Report for Equal Variance T-Test This report gives the power of the equal variance T-Test when it is assumed that the population mean and standard deviation are equal to the sample mean and standard deviation. Unequal Variance T-Test This section reports the details of the Aspin-Welch T-Test method for comparing two means when the variance of the two groups is assumed to be unequal. Power Report for Unequal Variance T-Test This report gives the power of the Aspin-Welch unequal variance T-Test when it is assumed that the population mean and standard deviation are equal to the sample mean and standard deviation. Z-Test This report gives the Z-Test for comparing two means assuming known variances. This procedure is rarely used in practice because the population variances are rarely known. σ1 This is the population standard deviation for Group 1. σ This is the population standard deviation for Group. 06-8

9 Tests - Nonparametric Randomization Test A randomization test is conducted by enumerating all possible permutations of the groups while leaving the data values in the original order. The difference is calculated for each permutation and the number of permutations that result in a difference with a magnitude greater than or equal to the actual difference is counted. Dividing this count by the number of permutations tried gives the significance level of the test. Monte Carlo Samples Specify the number of Monte Carlo samples used when conducting randomization tests. Somewhere between 1,000 and 100,000 are usually necessary. A value of 10,000 seems to be reasonable. Mann-Whitney Test This test is a nonparametric alternative to the equal-variance t-test for use when the assumption of normality is not valid. This test uses the ranks of the values rather than the values themselves. Kolmogorov-Smirnov Test This test is used to compare the distributions of two groups by comparing the empirical distribution functions of the two samples of the groups. The empirical distribution functions are compared by finding the greatest distance between the two. Approximate one-sided p-values are calculated by halving the corresponding two-sided p-value. Assumptions Tests of Assumptions This report gives a series of tests of assumptions. These tests are: Skewness Normality (for each group) Kurtosis Normality (for each group) Omnibus Normality (for each group) Variance Ratio Equal-Variance Test Modified Levene Equal Variance Test Assumptions Alpha Assumptions Alpha is the significance level used in all the assumptions tests. A value of 0.05 is typically used for hypothesis tests in general, but values other than 0.05 are often used for the case of testing assumptions. Typical values range from to 0.0. Report Options Tab The options on this panel control the label and decimal options of the reports. Report Options Variable Names This option lets you select whether to display only variable names, variable labels, or both. 06-9

10 Value Labels This option applies to the Group Variable(s). It lets you select whether to display data values, value labels, or both. Use this option if you want the output to automatically attach labels to the values (like 1=Yes, =No, etc.). See the section on specifying Value Labels elsewhere in this manual. Decimal Places Decimals This option allows the user to specify the number of decimal places directly or based on the significant digits. These decimals are used for output except the nonparametric tests and bootstrap results, where the decimal places are defined internally. If one of the significant digits options is used, the ending zero digits are not shown. For example, if 'Significant Digits (Up to 7)' is chosen, is displayed as is displayed as The output formatting system is not designed to accommodate 'Significant Digits (Up to 13)', and if chosen, this will likely lead to lines that run on to a second line. This option is included, however, for the rare case when a very large number of decimals is needed. Plots Tab The options on this panel control the inclusion and appearance of the plots. Select Plots Histogram Box Plot Check the boxes to display the plot. Click the plot format button to change the plot settings. For histograms and probability plots, two plots will be generated for each selection, one for Group 1 and one for Group. The box plot selection will produce a side-by-side box plot. Bootstrap Confidence Interval Histograms This plot requires that the bootstrap confidence intervals report has been selected. It gives plots of the bootstrap distributions of the two groups individually, as well as the bootstrap differences

11 Example 1 Comparing Two Groups This section presents an example of how to run a comparison of two groups. We will use the corn yield data found in YldA and YldB of the Yield dataset. You may follow along here by making the appropriate entries or load the completed template Example 1 by clicking on Open Example Template from the File menu of the window. 1 Open the Yield dataset. From the File menu of the NCSS Data window, select Open Example Data. Click on the file Yield.NCSS. Click Open. Open the window. Using the Analysis menu or the Procedure Navigator, find and select the procedure. On the menus, select File, then New Template. This will fill the procedure with the default template. 3 Specify the variables. Select the Variables tab. Set the Input Type to Two Variables with Response Data in each Variable. Click the small Column Selection button to the right of Group 1 Variable. Select YldA and click Ok. The column name YldA will appear in the Group 1 Variable box. Click the small Column Selection button to the right of Group Variable. Select YldB and click Ok. The column name YldB will appear in the Group Variable box. 4 Specify the reports. Select the Reports tab. Leave the Confidence Level at 95%. Make sure the Descriptive Statistics and Confidence Intervals of Each Group check box is checked. Make sure the Confidence Interval of µ1 µ check box is checked. Leave the Confidence Interval Limits at Two-Sided. Check Bootstrap Confidence Intervals. Leave the sub-options at the default values. Make sure the Confidence Intervals of Each Group Standard Deviation check box is checked. Make sure the Confidence Interval of σ1/σ check box is checked. Leave Alpha at Leave the H0 Value at 0.0. For Ha, select Two-Sided and One-Sided. Usually a single alternative hypothesis is chosen, but all three alternatives are shown in this example to exhibit all the reporting options. Make sure the Equal Variance T-Test check box is checked. Check the Power Report for Equal Variance T-Test check box. Make sure the Unequal Variance T-Test check box is checked. Check the Power Report for Unequal Variance T-Test check box. Check the Z-Test check box. Enter 150 for σ1, and 110 for σ. Check the Randomization Test check box. Leave the Monte Carlo Samples at Make sure the Mann-Whitney Test and Kolmogorov-Smirnov Test check boxes are checked. Make sure the Test of Assumptions check box is checked. Leave the Assumptions Alpha at Specify the plots. Select the Plots tab. Make sure all the plot check boxes are checked. 6 Run the procedure. From the Run menu, select Run Procedure. Alternatively, just click the green Run button

12 The following reports and charts will be displayed in the Output window. Descriptive Statistics Section This section gives a descriptive summary of each group. See the Descriptive Statistics chapter for details about this section. This report can be used to confirm that the Means and Counts are as expected. Standard Standard 95% LCL 95% UCL Variable Count Mean Deviation Error of Mean of Mean YldA YldB Note: T* (YldA) =.1788, T* (YldB) =.1314 Variable These are the names of the variables or groups. Count The count gives the number of non-missing values. This value is often referred to as the group sample size or n. Mean This is the average for each group. Standard Deviation The sample standard deviation is the square root of the sample variance. It is a measure of spread. Standard Error This is the estimated standard deviation for the distribution of sample means for an infinite population. It is the sample standard deviation divided by the square root of sample size, n. LCL of the Mean This is the lower limit of an interval estimate of the mean based on a Student s t distribution with n - 1 degrees of freedom. This interval estimate assumes that the population standard deviation is not known and that the data are normally distributed. UCL of the Mean This is the upper limit of the interval estimate for the mean based on a t distribution with n - 1 degrees of freedom. T* This is the t-value used to construct the confidence interval. If you were constructing the interval manually, you would obtain this value from a table of the Student s t distribution with n - 1 degrees of freedom. Descriptive Statistics for the Median Section This section gives the medians and the confidence intervals for the medians of each of the groups. 95.0% LCL 95.0% UCL Variable Count Median of Median of Median YldA YldB Variable These are the names of the variables or groups. 06-1

13 Count The count gives the number of non-missing values. This value is often referred to as the group sample size or n. Median The median is the 50 th percentile of the group data, using the AveXp(n+1) method. The details of this method are described in the Descriptive Statistics chapter under Percentile Type. LCL and UCL These are the lower and upper confidence limits of the median. These limits are exact and make no distributional assumptions other than a continuous distribution. No limits are reported if the algorithm for this interval is not able to find a solution. This may occur if the number of unique values is small. Two-Sided Confidence Interval for µ1 µ Section Given that the assumptions of independent samples and normality are valid, this section provides an interval estimate (confidence limits) of the difference between the two means. Results are given for both the equal and unequal variance cases. You can use the Tests of Assumptions section to assess the assumption of equal variance. 95.0% C. I. of μ1 - μ Variance Mean Standard Standard Lower Upper Assumption DF Difference Deviation Error T* Limit Limit Equal Unequal DF The degrees of freedom are used to determine the T distribution from which T* is generated. For the equal variance case: For the unequal variance case: dddd = nn 1 + nn ss 1 nn + ss dddd = 1 nn ss 1 ss nn 1 nn nn nn 1 Mean Difference This is the difference between the sample means, XX 1 XX. Standard Deviation In the equal variance case, this quantity is: In the unequal variance case, this quantity is: ss XX1 XX = (nn 1 1)ss 1 + (nn 1)ss nn 1 nn ss XX1 XX = ss 1 + ss 06-13

14 Standard Error This is the estimated standard deviation of the distribution of differences between independent sample means. For the equal variance case: For the unequal variance case: SSSS XX1 XX = (nn 1 1)ss 1 + (nn 1)ss nn 1 nn nn 1 nn SSSS XX1 XX = ss 1 + ss nn 1 T* This is the t-value used to construct the confidence limits. It is based on the degrees of freedom and the confidence level. Lower and Upper Confidence Limits These are the confidence limits of the confidence interval for μμ 1 μμ. The confidence interval formula is nn XX 1 XX ± TT dddd SSSS XX1 XX The equal-variance and unequal-variance assumption formulas differ by the values of T* and the standard error. Bootstrap Section Bootstrapping was developed to provide standard errors and confidence intervals in situations in which the standard assumptions are not valid. The method is simple in concept, but it requires extensive computation. In bootstrapping, the sample is assumed to be the population and B samples of size N1 are drawn from the original group one dataset and N from the original group dataset, with replacement. With replacement sampling means that each observation is placed back in the population before the next one is selected so that each observation may be selected more than once. For each bootstrap sample, the means and their difference are computed and stored. Suppose that you want the standard error and a confidence interval of the difference. The bootstrap sampling process provides B estimates of the difference. The standard deviation of these B differences is the bootstrap estimate of the standard error of the difference. The bootstrap confidence interval is found by arranging the B values in sorted order and selecting the appropriate percentiles from the list. For example, a 90% bootstrap confidence interval for the difference is given by fifth and ninety-fifth percentiles of the bootstrap difference values. The main assumption made when using the bootstrap method is that your sample approximates the population fairly well. Because of this assumption, bootstrapping does not work well for small samples in which there is little likelihood that the sample is representative of the population. Bootstrapping should only be used in medium to large samples

15 Estimation Results Bootstrap Confidence Limits Parameter Estimate Conf. Level Lower Upper Difference Original Value Bootstrap Mean Bias (BM - OV) Bias Corrected Standard Error Mean 1 Original Value Bootstrap Mean Bias (BM - OV) Bias Corrected Standard Error Mean Original Value Bootstrap Mean Bias (BM - OV) Bias Corrected Standard Error Confidence Interval Method = Reflection, Number of Samples = Bootstrap Histograms Section This report provides bootstrap confidence intervals of the two means and their difference. Note that since these results are based on 3000 random bootstrap samples, they will differ slightly from the results you obtain when you run this report. Original Value This is the parameter estimate obtained from the complete sample without bootstrapping. Bootstrap Mean This is the average of the parameter estimates of the bootstrap samples. Bias (BM - OV) This is an estimate of the bias in the original estimate. It is computed by subtracting the original value from the bootstrap mean. Bias Corrected This is an estimated of the parameter that has been corrected for its bias. The correction is made by subtracting the estimated bias from the original parameter estimate. Standard Error This is the bootstrap method s estimate of the standard error of the parameter estimate. It is simply the standard deviation of the parameter estimate computed from the bootstrap estimates

16 Conf. Level This is the confidence coefficient of the bootstrap confidence interval given to the right. Bootstrap Confidence Limits - Lower and Upper These are the limits of the bootstrap confidence interval with the confidence coefficient given to the left. These limits are computed using the confidence interval method (percentile or reflection) designated. Note that these intervals should be based on over a thousand bootstrap samples and the original sample must be representative of the population. Bootstrap Histogram The histogram shows the distribution of the bootstrap parameter estimates. Confidence Intervals of Standard Deviations Confidence Intervals of Standard Deviations 95.0% C. I. of σ Standard Standard Lower Upper Sample N Mean Deviation Error Limit Limit This report gives a confidence interval for the standard deviation in each group. Note that the usefulness of these intervals is very dependent on the assumption that the data are sampled from a normal distribution. Using the common notation for sample statistics (see, for example, ZAR (1984) page 115), a 100( 1 α )% confidence interval for the standard deviation is given by (nn 1)ss (nn 1)ss σσ χχ 1 αα/,nn 1 χχ αα/,nn 1 Confidence Interval for Standard Deviation Ratio Confidence Interval for Standard Deviation Ratio (σ1 / σ) 95.0% C. I. of σ1 / σ Lower Upper N1 N SD1 SD SD1 / SD Limit Limit This report gives a confidence interval for the ratio of the two standard deviations. Note that the usefulness of these intervals is very dependent on the assumption that the data are sampled from a normal distribution. Using the common notation for sample statistics (see, for example, ZAR (1984) page 15), a 100( 1 α )% confidence interval for the ratio of two standard deviations is given by ss 1 ss FF 1 αα/,nn1 1,nn 1 σσ 1 σσ ss 1 FF 1 αα/,nn 1,nn 1 1 ss 06-16

17 Equal-Variance T-Test Section This section presents the results of the traditional equal-variance T-test. Here, reports for all three alternative hypotheses are shown, but a researcher would typically choose one of the three before generating the output. All three tests are shown here for the purpose of exhibiting all the output options available. Equal-Variance T-Test μ1 - μ: (YldA) - (YldB) Standard Alternative Mean Error of Prob Reject H0 Hypothesis Difference Difference T-Statistic d.f. Level at α = μ1 - μ No μ1 - μ < No μ1 - μ > No Alternative Hypothesis The (unreported) null hypothesis is and the alternative hypotheses, HH 0 : μμ 1 μμ = Hypothesized Difference = 0 HH aa : μμ 1 μμ Hypothesized Difference = 0 HH aa : μμ 1 μμ < Hypothesized Difference = 0 HH aa : μμ 1 μμ > Hypothesized Difference = 0 Since the Hypothesized Difference is zero in this example, the null and alternative hypotheses can be simplified to Null hypothesis: Alternative hypotheses: HH 0 : μμ 1 = μμ HH aa : μμ 1 μμ, HH aa : μμ 1 < μμ, or HH aa : μμ 1 > μμ. In practice, the alternative hypothesis should be chosen in advance. Mean Difference This is the difference between the sample means, XX 1 XX. Standard Error of Difference This is the estimated standard deviation of the distribution of differences between independent sample means. The formula for the standard error of the difference in the equal-variance T-test is: SSSS XX1 XX = (nn 1 1)ss 1 + (nn 1)ss nn 1 nn nn 1 nn T-Statistic The T-Statistic is the value used to produce the p-value (Prob Level) based on the T distribution. The formula for the T-Statistic is: TT SSSSSSSSiiiiiiiiii = XX 1 XX HHHHHHHHHHheeeeeeeeeeee DDDDDDDDDDDDDDDDDDDD SSSS XX1 XX 06-17

18 In this instance, the hypothesized difference is zero, so the T-Statistic formula reduces to TT SSSSSSSSSSSSSSSSSS = XX 1 XX SSSS XX1 XX d.f. The degrees of freedom define the T distribution upon which the probability values are based. The formula for the degrees of freedom in the equal-variance T-test is: dddd = nn 1 + nn Prob Level The probability level, also known as the p-value or significance level, is the probability that the test statistic will take a value at least as extreme as the observed value, assuming that the null hypothesis is true. If the p-value is less than the prescribed α, in this case 0.05, the null hypothesis is rejected in favor of the alternative hypothesis. Otherwise, there is not sufficient evidence to reject the null hypothesis. Reject H0 at α = (0.050) This column indicates whether or not the null hypothesis is rejected, in favor of the alternative hypothesis, based on the p-value and chosen α. A test in which the null hypothesis is rejected is sometimes called significant. Power for the Equal-Variance T-Test The power report gives the power of a test where it is assumed that the population means and standard deviations are equal to the sample means and standard deviations. Powers are given for alpha values of 0.05 and For a much more comprehensive and flexible investigation of power or sample size, we recommend you use the PASS software program. Power for the Equal-Variance T-Test This section assumes the population means and standard deviations are equal to the sample values. μ1 - μ: (YldA) - (YldB) Alternative Power Power Hypothesis N1 N μ1 μ σ1 σ (α = 0.05) (α = 0.01) μ1 - μ μ1 - μ < μ1 - μ > Alternative Hypothesis This value identifies the test direction of the test reported in this row. In practice, you would select the alternative hypothesis prior to your analysis and have only one row showing here. N1 and N N1 and N are the assumed sample sizes for groups 1 and. µ1 and µ These are the assumed population means on which the power calculations are based. σ1 and σ These are the assumed population standard deviations on which the power calculations are based. Power (α = 0.05) and Power (α = 0.01) Power is the probability of rejecting the hypothesis that the means are equal when they are in fact not equal. Power is one minus the probability of a type II error (β). The power of the test depends on the sample size, the magnitudes of the standard deviations, the alpha level, and the true difference between the two population means

19 The power value calculated here assumes that the population standard deviations are equal to the sample standard deviations and that the difference between the population means is exactly equal to the difference between the sample means. High power is desirable. High power means that there is a high probability of rejecting the null hypothesis when the null hypothesis is false. Some ways to increase the power of a test include the following: 1. Increase the alpha level. Perhaps you could test at alpha = 0.05 instead of alpha = Increase the sample size. 3. Decrease the magnitude of the standard deviations. Perhaps the study can be redesigned so that measurements are more precise and extraneous sources of variation are removed. Aspin-Welch Unequal-Variance T-Test Section This section presents the results of the traditional equal-variance T-test. Here, reports for all three alternative hypotheses are shown, but a researcher would typically choose one of the three before generating the output. All three tests are shown here for the purpose of exhibiting all the output options available. Aspin-Welch Unequal-Variance T-Test μ1 - μ: (YldA) - (YldB) Standard Alternative Mean Error of Prob Reject H0 Hypothesis Difference Difference T-Statistic d.f. Level at α = μ1 - μ No μ1 - μ < No μ1 - μ > No Alternative Hypothesis The (unreported) null hypothesis is and the alternative hypotheses, HH 0 : μμ 1 μμ = Hypothesized Difference = 0 HH aa : μμ 1 μμ Hypothesized Difference = 0 HH aa : μμ 1 μμ < Hypothesized Difference = 0 HH aa : μμ 1 μμ > Hypothesized Difference = 0 Since the Hypothesized Difference is zero in this example, the null and alternative hypotheses can be simplified to Null hypothesis: Alternative hypotheses: HH 0 : μμ 1 = μμ HH aa : μμ 1 μμ, HH aa : μμ 1 < μμ, or HH aa : μμ 1 > μμ. In practice, the alternative hypothesis should be chosen in advance. Mean Difference This is the difference between the sample means, XX 1 XX

20 Standard Error of Difference This is the estimated standard deviation of the distribution of differences between independent sample means. The formula for the standard error of the difference in the Aspin-Welch unequal-variance T-test is: SSSS XX1 XX = ss 1 + ss nn 1 T-Statistic The T-Statistic is the value used to produce the p-value (Prob Level) based on the T distribution. The formula for the T-Statistic is: TT SSSSSSSSSSSSSSSSSS = XX 1 XX HHHHHHHHHHheeeeeeeeeeee DDDDDDDDDDDDDDDDDDDD SSSS XX1 XX In this instance, the hypothesized difference is zero, so the T-Statistic formula reduces to nn TT SSSSSSSSSSssssssss = XX 1 XX SSSS XX1 XX d.f. The degrees of freedom define the T distribution upon which the probability values are based. The formula for the degrees of freedom in the Aspin-Welch unequal-variance T-test is: ss 1 + ss nn dddd = 1 nn ss 1 ss nn 1 nn nn nn 1 Prob Level The probability level, also known as the p-value or significance level, is the probability that the test statistic will take a value at least as extreme as the observed value, assuming that the null hypothesis is true. If the p-value is less than the prescribed α, in this case 0.05, the null hypothesis is rejected in favor of the alternative hypothesis. Otherwise, there is not sufficient evidence to reject the null hypothesis. Reject H0 at α = (0.050) This column indicates whether or not the null hypothesis is rejected, in favor of the alternative hypothesis, based on the p-value and chosen α. A test in which the null hypothesis is rejected is sometimes called significant. 06-0

21 Power for the Aspin-Welch Unequal-Variance T-Test The power report gives the power of a test where it is assumed that the population means and standard deviations are equal to the sample means and standard deviations. Powers are given for alpha values of 0.05 and For a much more comprehensive and flexible investigation of power or sample size, we recommend you use the PASS software program. Power for the Aspin-Welch Unequal-Variance T-Test This section assumes the population means and standard deviations are equal to the sample values. μ1 - μ: (YldA) - (YldB) Alternative Power Power Hypothesis N1 N μ1 μ σ1 σ (α = 0.05) (α = 0.01) μ1 - μ μ1 - μ < μ1 - μ > Alternative Hypothesis This value identifies the test direction of the test reported in this row. In practice, you would select the alternative hypothesis prior to your analysis and have only one row showing here. N1 and N N1 and N are the assumed sample sizes for groups 1 and. µ1 and µ These are the assumed population means on which the power calculations are based. σ1 and σ These are the assumed population standard deviations on which the power calculations are based. Power (α = 0.05) and Power (α = 0.01) Power is the probability of rejecting the hypothesis that the means are equal when they are in fact not equal. Power is one minus the probability of a type II error (β). The power of the test depends on the sample size, the magnitudes of the standard deviations, the alpha level, and the true difference between the two population means. The power value calculated here assumes that the population standard deviations are equal to the sample standard deviations and that the difference between the population means is exactly equal to the difference between the sample means. High power is desirable. High power means that there is a high probability of rejecting the null hypothesis when the null hypothesis is false. Some ways to increase the power of a test include the following: 1. Increase the alpha level. Perhaps you could test at alpha = 0.05 instead of alpha = Increase the sample size. 3. Decrease the magnitude of the standard deviations. Perhaps the study can be redesigned so that measurements are more precise and extraneous sources of variation are removed. 06-1

22 Z-Test Section This section presents the results of the two-sample Z-test, which is used when the population standard deviations are known. Because the population standard deviations are rarely known, this test is not commonly used in practice. The Z-test is included in this procedure for the less-common case of known standard deviations, and for two-sample hypothesis test training. In this example, reports for all three alternative hypotheses are shown, but a researcher would typically choose one of the three before generating the output. All three tests are shown here for the purpose of exhibiting all the output options available. Z-Test μ1 - μ: (YldA) - (YldB) Standard Alternative Mean Error of Prob Reject H0 Hypothesis Difference σ1 σ Difference Z-Statistic Level at α = μ1 - μ No μ1 - μ < No μ1 - μ > No Alternative Hypothesis The (unreported) null hypothesis is and the alternative hypotheses, HH 0 : μμ 1 μμ = Hypothesized Difference = 0 HH aa : μμ 1 μμ Hypothesized Difference = 0 HH aa : μμ 1 μμ < Hypothesized Difference = 0 HH aa : μμ 1 μμ > Hypothesized Difference = 0 Since the Hypothesized Difference is zero in this example, the null and alternative hypotheses can be simplified to Null hypothesis: Alternative hypotheses: HH 0 : μμ 1 = μμ HH aa : μμ 1 μμ, HH aa : μμ 1 < μμ, or HH aa : μμ 1 > μμ. In practice, the alternative hypothesis should be chosen in advance. Mean Difference This is the difference between the sample means, XX 1 XX. σ1 and σ These are the known Group 1 and Group population standard deviations. Standard Error of Difference This is the estimated standard deviation of the distribution of differences between independent sample means. The formula for the standard error of the difference in the Z-test is: SSSS XX1 XX = σσ 1 + σσ nn 1 nn 06-

23 Z-Statistic The Z-Statistic is the value used to produce the p-value (Prob Level) based on the Z distribution. The formula for the Z-Statistic is: ZZ SSSSSSSSSSSSSSSSSS = XX 1 XX HHHHHHHHHHheeeeeeeeeeee DDDDDDDDDDDDDDDDDDDD SSSS XX1 XX In this instance, the hypothesized difference is zero, so the Z-Statistic formula reduces to ZZ SSSSSSSSSSSSSSSSSS = XX 1 XX SSSS XX1 XX Prob Level The probability level, also known as the p-value or significance level, is the probability that the test statistic will take a value at least as extreme as the observed value, assuming that the null hypothesis is true. If the p-value is less than the prescribed α, in this case 0.05, the null hypothesis is rejected in favor of the alternative hypothesis. Otherwise, there is not sufficient evidence to reject the null hypothesis. Reject H0 at α = (0.050) This column indicates whether or not the null hypothesis is rejected, in favor of the alternative hypothesis, based on the p-value and chosen α. A test in which the null hypothesis is rejected is sometimes called significant. Randomization Tests Section The Randomization test is a non-parametric test for comparing two distributions. A randomization test is conducted by enumerating all possible permutations of the groups while leaving the data values in the original order. The appropriate statistic (here, each of the two t-statistics) is calculated for each permutation and the number of permutations that result in a statistic with a magnitude greater than or equal to the statistic is counted. Dividing this count by the number of permutations tried gives the significance level of the test. For even moderate sample sizes, the total number of permutations is in the trillions, so a Monte Carlo approach is used in which the permutations are found by random selection rather than complete enumeration. Edgington (1987) suggests that at least 1,000 permutations by selected. We suggest that current computer speeds permit the number of permutations selected to be increased to 10,000 or more. Randomization Tests Alternative Hypothesis: There is a treatment effect for (YldA) - (YldB). Number of Monte Carlo samples: Variance Prob Reject H0 Assumption Level at α = Equal Variance No Unequal Variance No Variance Assumption The variance assumption distinguishes which T-statistic formula is used to create the distribution of T-statistics. If the equal variance assumption is chosen the T-statistics are based on the formula: TT SSSSSSSSSSSSSSSSSS = XX 1 XX HHHHHHHHHHheeeeeeeeeeee DDDDDDDDDDDDDDDDDDDD (nn 1 1)ss 1 + (nn 1)ss nn 1 nn 1 nn nn 06-3

24 If the unequal variance assumption is chosen the T-statistics are based on the formula: TT SSSSSSSSSSSSSSSSSS = XX 1 XX HHHHHHHHHHheeeeeeeeeeee DDDDDDDDDDDDDDDDDDDD ss 1 nn + ss 1 Prob Level The probability level, also known as the p-value or significance level, is the probability that the test statistic will take a value at least as extreme as the observed value, assuming that the null hypothesis is true. The Prob Level for the randomization test is calculated by finding the proportion of the permutation t-statistics that are more extreme than the original data t-statistic. If the p-value is less than the prescribed α, in this case 0.05, the null hypothesis is rejected in favor of the alternative hypothesis. Otherwise, there is not sufficient evidence to reject the null hypothesis. Reject H0 at α = (0.050) This column indicates whether or not the null hypothesis is rejected, in favor of the alternative hypothesis, based on the p-value and chosen α. A test in which the null hypothesis is rejected is sometimes called significant. For a randomization test, a significant p-value does not necessarily indicate a difference in means, but instead an effect of some kind from a difference in group populations. nn Mann-Whitney U or Wilcoxon Rank-Sum Test This test is the most common nonparametric substitute for the t-test when the assumption of normality is not valid. The assumptions for this test were given in the Assumptions section at the beginning of this chapter. Two key assumptions are that the distributions are at least ordinal in nature and that they are identical, except for location. When ties are present in the data, an approximate solution for dealing with ties is available, you can use the approximation provided, but know that the exact results no longer hold. This particular test is based on ranks and has good properties (asymptotic relative efficiency) for symmetric distributions. There are exact procedures for this test given small samples with no ties, and there are large sample approximations. Mann-Whitney U or Wilcoxon Rank-Sum Test for Difference in Location Mann W Mean Std Dev Variable Whitney U Sum Ranks of W of W YldA YldB Number Sets of Ties = 3, Multiplicity Factor = 18 Variable This is the name for each sample, group, or treatment. Mann Whitney U The Mann-Whitney test statistic, U, is defined as the total number of times an observation in one group is preceded by an observation in the other group in the ordered configuration of combined samples (Gibbons, 1985). It can be found by examining the observations in a group one-by-one, counting the number of observations in the other group that have smaller rank, and then finding the total. Or it can be found by the formula UU 1 = WW 1 nn 1(nn 1 + 1) for Group 1, and 06-4

25 UU = WW nn (nn + 1) for Group, where WW 1 is the sum of the ranks in sample 1, and WW is the sum of the ranks in sample. W Sum Ranks The W statistic is obtained by combining all observations from both groups, assigning ranks, and then summing the ranks, WW 1 = rrrrrrrrrr 1, WW = rrrrrrrrrr Tied values are resolved by using the average rank of the tied values. Mean of W This is the mean of the distribution of W, formulated as follows: and WW 1 = nn 1(nn 1 + nn + 1) WW = nn (nn 1 + nn + 1) Std Dev of W This is the standard deviation of the W corrected for ties. If there are no ties, this standard deviation formula simplifies since the second term under the radical is zero. σσ WW = nn 1nn (nn 1 + nn + 1) 1 nn 1 nn ii=1 tt 3 ii tt ii 1(nn 1 + nn )(nn 1 + nn 1) where t 1 is the number of observations tied at value one, t is the number of observations tied at some value two, and so forth. Generally, this correction for ties in the standard deviation makes little difference unless there is a large number of ties. Number Sets of Ties This gives the number of sets of tied values. If there are no ties, this number is zero. A set of ties is two or more observations with the same value. This is used in adjusting the standard deviation for the W. Multiplicity factor This is the tie portion of the standard deviation of W, given by tt ii 3 tt ii Mann-Whitney U or Wilcoxon Rank-Sum Test for Difference in Location (continued) ii=1 Exact Probability* Approx. Without Correction Approx. With Correction Alternative Prob Reject H0 Prob Reject H0 Prob Reject H0 Hypothesis Level (α = 0.050) Z-Value Level (α = 0.050) Z-Value Level (α = 0.050) Diff No No Diff < No No Diff > No No *Exact probabilities are given only when there are no ties. Alternative Hypothesis For the Wilcoxon rank-sum test, the null and alternative hypotheses relate to the equality or non-equality of the central tendency of the two distributions. If a hypothesized difference other than zero is used, the software adds 06-5

### NCSS Statistical Software Chapter 06 Introduction This procedure provides several reports for the comparison of two distributions, including confidence intervals for the difference in means, two-sample t-tests, the z-test, the

### NCSS Statistical Software. One-Sample T-Test Chapter 205 Introduction This procedure provides several reports for making inference about a population mean based on a single sample. These reports include confidence intervals of the mean or median,

### Paired T-Test. Chapter 208. Introduction. Technical Details. Research Questions Chapter 208 Introduction This procedure provides several reports for making inference about the difference between two population means based on a paired sample. These reports include confidence intervals

### Two-Sample T-Tests Assuming Equal Variance (Enter Means) Chapter 4 Two-Sample T-Tests Assuming Equal Variance (Enter Means) Introduction This procedure provides sample size and power calculations for one- or two-sided two-sample t-tests when the variances of

### Two-Sample T-Tests Allowing Unequal Variance (Enter Difference) Chapter 45 Two-Sample T-Tests Allowing Unequal Variance (Enter Difference) Introduction This procedure provides sample size and power calculations for one- or two-sided two-sample t-tests when no assumption

### Confidence Intervals for the Difference Between Two Means Chapter 47 Confidence Intervals for the Difference Between Two Means Introduction This procedure calculates the sample size necessary to achieve a specified distance from the difference in sample means

### LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING In this lab you will explore the concept of a confidence interval and hypothesis testing through a simulation problem in engineering setting.

### Non-Inferiority Tests for Two Means using Differences Chapter 450 on-inferiority Tests for Two Means using Differences Introduction This procedure computes power and sample size for non-inferiority tests in two-sample designs in which the outcome is a continuous

### Pearson's Correlation Tests Chapter 800 Pearson's Correlation Tests Introduction The correlation coefficient, ρ (rho), is a popular statistic for describing the strength of the relationship between two variables. The correlation

### Lesson 1: Comparison of Population Means Part c: Comparison of Two- Means Lesson : Comparison of Population Means Part c: Comparison of Two- Means Welcome to lesson c. This third lesson of lesson will discuss hypothesis testing for two independent means. Steps in Hypothesis

### Tests for Two Proportions Chapter 200 Tests for Two Proportions Introduction This module computes power and sample size for hypothesis tests of the difference, ratio, or odds ratio of two independent proportions. The test statistics

### Comparing Means in Two Populations Comparing Means in Two Populations Overview The previous section discussed hypothesis testing when sampling from a single population (either a single mean or two means from the same population). Now we

### Standard Deviation Estimator CSS.com Chapter 905 Standard Deviation Estimator Introduction Even though it is not of primary interest, an estimate of the standard deviation (SD) is needed when calculating the power or sample size of

### Data Analysis Tools. Tools for Summarizing Data Data Analysis Tools This section of the notes is meant to introduce you to many of the tools that are provided by Excel under the Tools/Data Analysis menu item. If your computer does not have that tool

### Point Biserial Correlation Tests Chapter 807 Point Biserial Correlation Tests Introduction The point biserial correlation coefficient (ρ in this chapter) is the product-moment correlation calculated between a continuous random variable

### NCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( ) Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates

### Recall this chart that showed how most of our course would be organized: Chapter 4 One-Way ANOVA Recall this chart that showed how most of our course would be organized: Explanatory Variable(s) Response Variable Methods Categorical Categorical Contingency Tables Categorical

### Part 3. Comparing Groups. Chapter 7 Comparing Paired Groups 189. Chapter 8 Comparing Two Independent Groups 217 Part 3 Comparing Groups Chapter 7 Comparing Paired Groups 189 Chapter 8 Comparing Two Independent Groups 217 Chapter 9 Comparing More Than Two Groups 257 188 Elementary Statistics Using SAS Chapter 7 Comparing

### Confidence Intervals for One Standard Deviation Using Standard Deviation Chapter 640 Confidence Intervals for One Standard Deviation Using Standard Deviation Introduction This routine calculates the sample size necessary to achieve a specified interval width or distance from

### Confidence Intervals for Cp Chapter 296 Confidence Intervals for Cp Introduction This routine calculates the sample size needed to obtain a specified width of a Cp confidence interval at a stated confidence level. Cp is a process

### THE FIRST SET OF EXAMPLES USE SUMMARY DATA... EXAMPLE 7.2, PAGE 227 DESCRIBES A PROBLEM AND A HYPOTHESIS TEST IS PERFORMED IN EXAMPLE 7. THERE ARE TWO WAYS TO DO HYPOTHESIS TESTING WITH STATCRUNCH: WITH SUMMARY DATA (AS IN EXAMPLE 7.17, PAGE 236, IN ROSNER); WITH THE ORIGINAL DATA (AS IN EXAMPLE 8.5, PAGE 301 IN ROSNER THAT USES DATA FROM

### Nonparametric Two-Sample Tests. Nonparametric Tests. Sign Test Nonparametric Two-Sample Tests Sign test Mann-Whitney U-test (a.k.a. Wilcoxon two-sample test) Kolmogorov-Smirnov Test Wilcoxon Signed-Rank Test Tukey-Duckworth Test 1 Nonparametric Tests Recall, nonparametric

### Two Related Samples t Test Two Related Samples t Test In this example 1 students saw five pictures of attractive people and five pictures of unattractive people. For each picture, the students rated the friendliness of the person

### Non-Inferiority Tests for One Mean Chapter 45 Non-Inferiority ests for One Mean Introduction his module computes power and sample size for non-inferiority tests in one-sample designs in which the outcome is distributed as a normal random

### t-test Statistics Overview of Statistical Tests Assumptions t-test Statistics Overview of Statistical Tests Assumption: Testing for Normality The Student s t-distribution Inference about one mean (one sample t-test) Inference about two means (two sample t-test)

### Permutation Tests for Comparing Two Populations Permutation Tests for Comparing Two Populations Ferry Butar Butar, Ph.D. Jae-Wan Park Abstract Permutation tests for comparing two populations could be widely used in practice because of flexibility of

### INTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA) INTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA) As with other parametric statistics, we begin the one-way ANOVA with a test of the underlying assumptions. Our first assumption is the assumption of

### THE KRUSKAL WALLLIS TEST THE KRUSKAL WALLLIS TEST TEODORA H. MEHOTCHEVA Wednesday, 23 rd April 08 THE KRUSKAL-WALLIS TEST: The non-parametric alternative to ANOVA: testing for difference between several independent groups 2 NON

### Scatter Plots with Error Bars Chapter 165 Scatter Plots with Error Bars Introduction The procedure extends the capability of the basic scatter plot by allowing you to plot the variability in Y and X corresponding to each point. Each

### Lin s Concordance Correlation Coefficient NSS Statistical Software NSS.com hapter 30 Lin s oncordance orrelation oefficient Introduction This procedure calculates Lin s concordance correlation coefficient ( ) from a set of bivariate data. The

### Non-Inferiority Tests for Two Proportions Chapter 0 Non-Inferiority Tests for Two Proportions Introduction This module provides power analysis and sample size calculation for non-inferiority and superiority tests in twosample designs in which

### Confidence Intervals for Cpk Chapter 297 Confidence Intervals for Cpk Introduction This routine calculates the sample size needed to obtain a specified width of a Cpk confidence interval at a stated confidence level. Cpk is a process

### Tests for One Proportion Chapter 100 Tests for One Proportion Introduction The One-Sample Proportion Test is used to assess whether a population proportion (P1) is significantly different from a hypothesized value (P0). This is

### Randomized Block Analysis of Variance Chapter 565 Randomized Block Analysis of Variance Introduction This module analyzes a randomized block analysis of variance with up to two treatment factors and their interaction. It provides tables of

### CALCULATIONS & STATISTICS CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 1-5 scale to 0-100 scores When you look at your report, you will notice that the scores are reported on a 0-100 scale, even though respondents

### Testing for differences I exercises with SPSS Testing for differences I exercises with SPSS Introduction The exercises presented here are all about the t-test and its non-parametric equivalents in their various forms. In SPSS, all these tests can

### Tutorial 5: Hypothesis Testing Tutorial 5: Hypothesis Testing Rob Nicholls nicholls@mrc-lmb.cam.ac.uk MRC LMB Statistics Course 2014 Contents 1 Introduction................................ 1 2 Testing distributional assumptions....................

### Using Excel for inferential statistics FACT SHEET Using Excel for inferential statistics Introduction When you collect data, you expect a certain amount of variation, just caused by chance. A wide variety of statistical tests can be applied

### Good luck! BUSINESS STATISTICS FINAL EXAM INSTRUCTIONS. Name: Glo bal Leadership M BA BUSINESS STATISTICS FINAL EXAM Name: INSTRUCTIONS 1. Do not open this exam until instructed to do so. 2. Be sure to fill in your name before starting the exam. 3. You have two hours Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition Online Learning Centre Technology Step-by-Step - Excel Microsoft Excel is a spreadsheet software application

### Lecture Notes Module 1 Lecture Notes Module 1 Study Populations A study population is a clearly defined collection of people, animals, plants, or objects. In psychological research, a study population usually consists of a specific

### Skewed Data and Non-parametric Methods 0 2 4 6 8 10 12 14 Skewed Data and Non-parametric Methods Comparing two groups: t-test assumes data are: 1. Normally distributed, and 2. both samples have the same SD (i.e. one sample is simply shifted

### Gamma Distribution Fitting Chapter 552 Gamma Distribution Fitting Introduction This module fits the gamma probability distributions to a complete or censored set of individual or grouped data values. It outputs various statistics

### MBA 611 STATISTICS AND QUANTITATIVE METHODS MBA 611 STATISTICS AND QUANTITATIVE METHODS Part I. Review of Basic Statistics (Chapters 1-11) A. Introduction (Chapter 1) Uncertainty: Decisions are often based on incomplete information from uncertain

### Two Correlated Proportions (McNemar Test) Chapter 50 Two Correlated Proportions (Mcemar Test) Introduction This procedure computes confidence intervals and hypothesis tests for the comparison of the marginal frequencies of two factors (each with

### Study Guide for the Final Exam Study Guide for the Final Exam When studying, remember that the computational portion of the exam will only involve new material (covered after the second midterm), that material from Exam 1 will make

### Tests for Two Survival Curves Using Cox s Proportional Hazards Model Chapter 730 Tests for Two Survival Curves Using Cox s Proportional Hazards Model Introduction A clinical trial is often employed to test the equality of survival distributions of two treatment groups.

### 1 SAMPLE SIGN TEST. Non-Parametric Univariate Tests: 1 Sample Sign Test 1. A non-parametric equivalent of the 1 SAMPLE T-TEST. Non-Parametric Univariate Tests: 1 Sample Sign Test 1 1 SAMPLE SIGN TEST A non-parametric equivalent of the 1 SAMPLE T-TEST. ASSUMPTIONS: Data is non-normally distributed, even after log transforming.

### Non-Parametric Tests (I) Lecture 5: Non-Parametric Tests (I) KimHuat LIM lim@stats.ox.ac.uk http://www.stats.ox.ac.uk/~lim/teaching.html Slide 1 5.1 Outline (i) Overview of Distribution-Free Tests (ii) Median Test for Two Independent

### Testing Group Differences using T-tests, ANOVA, and Nonparametric Measures Testing Group Differences using T-tests, ANOVA, and Nonparametric Measures Jamie DeCoster Department of Psychology University of Alabama 348 Gordon Palmer Hall Box 870348 Tuscaloosa, AL 35487-0348 Phone:

### Stepwise Regression. Chapter 311. Introduction. Variable Selection Procedures. Forward (Step-Up) Selection Chapter 311 Introduction Often, theory and experience give only general direction as to which of a pool of candidate variables (including transformed variables) should be included in the regression model.

### Projects Involving Statistics (& SPSS) Projects Involving Statistics (& SPSS) Academic Skills Advice Starting a project which involves using statistics can feel confusing as there seems to be many different things you can do (charts, graphs,

### KSTAT MINI-MANUAL. Decision Sciences 434 Kellogg Graduate School of Management KSTAT MINI-MANUAL Decision Sciences 434 Kellogg Graduate School of Management Kstat is a set of macros added to Excel and it will enable you to do the statistics required for this course very easily. To

### Biostatistics: DESCRIPTIVE STATISTICS: 2, VARIABILITY Biostatistics: DESCRIPTIVE STATISTICS: 2, VARIABILITY 1. Introduction Besides arriving at an appropriate expression of an average or consensus value for observations of a population, it is important to

### Descriptive Statistics Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize

### SCHOOL OF HEALTH AND HUMAN SCIENCES DON T FORGET TO RECODE YOUR MISSING VALUES SCHOOL OF HEALTH AND HUMAN SCIENCES Using SPSS Topics addressed today: 1. Differences between groups 2. Graphing Use the s4data.sav file for the first part of this session. DON T FORGET TO RECODE YOUR

### Statistics. One-two sided test, Parametric and non-parametric test statistics: one group, two groups, and more than two groups samples Statistics One-two sided test, Parametric and non-parametric test statistics: one group, two groups, and more than two groups samples February 3, 00 Jobayer Hossain, Ph.D. & Tim Bunnell, Ph.D. Nemours

### MEASURES OF LOCATION AND SPREAD Paper TU04 An Overview of Non-parametric Tests in SAS : When, Why, and How Paul A. Pappas and Venita DePuy Durham, North Carolina, USA ABSTRACT Most commonly used statistical procedures are based on the

### t Tests in Excel The Excel Statistical Master By Mark Harmon Copyright 2011 Mark Harmon t-tests in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com www.excelmasterseries.com

### UNDERSTANDING THE INDEPENDENT-SAMPLES t TEST UNDERSTANDING The independent-samples t test evaluates the difference between the means of two independent or unrelated groups. That is, we evaluate whether the means for two independent groups are significantly

### Dongfeng Li. Autumn 2010 Autumn 2010 Chapter Contents Some statistics background; ; Comparing means and proportions; variance. Students should master the basic concepts, descriptive statistics measures and graphs, basic hypothesis

### EPS 625 INTERMEDIATE STATISTICS FRIEDMAN TEST EPS 625 INTERMEDIATE STATISTICS The Friedman test is an extension of the Wilcoxon test. The Wilcoxon test can be applied to repeated-measures data if participants are assessed on two occasions or conditions

### One-Way ANOVA using SPSS 11.0. SPSS ANOVA procedures found in the Compare Means analyses. Specifically, we demonstrate 1 One-Way ANOVA using SPSS 11.0 This section covers steps for testing the difference between three or more group means using the SPSS ANOVA procedures found in the Compare Means analyses. Specifically,

### Summary of Formulas and Concepts. Descriptive Statistics (Ch. 1-4) Summary of Formulas and Concepts Descriptive Statistics (Ch. 1-4) Definitions Population: The complete set of numerical information on a particular quantity in which an investigator is interested. We assume

### 3.4 Statistical inference for 2 populations based on two samples 3.4 Statistical inference for 2 populations based on two samples Tests for a difference between two population means The first sample will be denoted as X 1, X 2,..., X m. The second sample will be denoted

### The Wilcoxon Rank-Sum Test 1 The Wilcoxon Rank-Sum Test The Wilcoxon rank-sum test is a nonparametric alternative to the twosample t-test which is based solely on the order in which the observations from the two samples fall. We

### SPSS Tests for Versions 9 to 13 SPSS Tests for Versions 9 to 13 Chapter 2 Descriptive Statistic (including median) Choose Analyze Descriptive statistics Frequencies... Click on variable(s) then press to move to into Variable(s): list

### NONPARAMETRIC STATISTICS 1. depend on assumptions about the underlying distribution of the data (or on the Central Limit Theorem) NONPARAMETRIC STATISTICS 1 PREVIOUSLY parametric statistics in estimation and hypothesis testing... construction of confidence intervals computing of p-values classical significance testing depend on assumptions

### 1.5 Oneway Analysis of Variance Statistics: Rosie Cornish. 200. 1.5 Oneway Analysis of Variance 1 Introduction Oneway analysis of variance (ANOVA) is used to compare several means. This method is often used in scientific or medical experiments

### Final Exam Practice Problem Answers Final Exam Practice Problem Answers The following data set consists of data gathered from 77 popular breakfast cereals. The variables in the data set are as follows: Brand: The brand name of the cereal

### Introduction. Hypothesis Testing. Hypothesis Testing. Significance Testing Introduction Hypothesis Testing Mark Lunt Arthritis Research UK Centre for Ecellence in Epidemiology University of Manchester 13/10/2015 We saw last week that we can never know the population parameters

### Confidence Intervals for Exponential Reliability Chapter 408 Confidence Intervals for Exponential Reliability Introduction This routine calculates the number of events needed to obtain a specified width of a confidence interval for the reliability (proportion

### StatCrunch and Nonparametric Statistics StatCrunch and Nonparametric Statistics You can use StatCrunch to calculate the values of nonparametric statistics. It may not be obvious how to enter the data in StatCrunch for various data sets that

### Part 2: Analysis of Relationship Between Two Variables Part 2: Analysis of Relationship Between Two Variables Linear Regression Linear correlation Significance Tests Multiple regression Linear Regression Y = a X + b Dependent Variable Independent Variable

### Research Methodology: Tools MSc Business Administration Research Methodology: Tools Applied Data Analysis (with SPSS) Lecture 11: Nonparametric Methods May 2014 Prof. Dr. Jürg Schwarz Lic. phil. Heidi Bruderer Enzler Contents Slide

### 6 3 The Standard Normal Distribution 290 Chapter 6 The Normal Distribution Figure 6 5 Areas Under a Normal Distribution Curve 34.13% 34.13% 2.28% 13.59% 13.59% 2.28% 3 2 1 + 1 + 2 + 3 About 68% About 95% About 99.7% 6 3 The Distribution Since

### 1. What is the critical value for this 95% confidence interval? CV = z.025 = invnorm(0.025) = 1.96 1 Final Review 2 Review 2.1 CI 1-propZint Scenario 1 A TV manufacturer claims in its warranty brochure that in the past not more than 10 percent of its TV sets needed any repair during the first two years

### Once saved, if the file was zipped you will need to unzip it. For the files that I will be posting you need to change the preferences. 1 Commands in JMP and Statcrunch Below are a set of commands in JMP and Statcrunch which facilitate a basic statistical analysis. The first part concerns commands in JMP, the second part is for analysis

### Sample Size and Power in Clinical Trials Sample Size and Power in Clinical Trials Version 1.0 May 011 1. Power of a Test. Factors affecting Power 3. Required Sample Size RELATED ISSUES 1. Effect Size. Test Statistics 3. Variation 4. Significance

### Binary Diagnostic Tests Two Independent Samples Chapter 537 Binary Diagnostic Tests Two Independent Samples Introduction An important task in diagnostic medicine is to measure the accuracy of two diagnostic tests. This can be done by comparing summary

### Confidence Intervals for Spearman s Rank Correlation Chapter 808 Confidence Intervals for Spearman s Rank Correlation Introduction This routine calculates the sample size needed to obtain a specified width of Spearman s rank correlation coefficient confidence Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm

### Difference of Means and ANOVA Problems Difference of Means and Problems Dr. Tom Ilvento FREC 408 Accounting Firm Study An accounting firm specializes in auditing the financial records of large firm It is interested in evaluating its fee structure,particularly

### BA 275 Review Problems - Week 6 (10/30/06-11/3/06) CD Lessons: 53, 54, 55, 56 Textbook: pp. 394-398, 404-408, 410-420 BA 275 Review Problems - Week 6 (10/30/06-11/3/06) CD Lessons: 53, 54, 55, 56 Textbook: pp. 394-398, 404-408, 410-420 1. Which of the following will increase the value of the power in a statistical test

### Statistics I for QBIC. Contents and Objectives. Chapters 1 7. Revised: August 2013 Statistics I for QBIC Text Book: Biostatistics, 10 th edition, by Daniel & Cross Contents and Objectives Chapters 1 7 Revised: August 2013 Chapter 1: Nature of Statistics (sections 1.1-1.6) Objectives

### Calculating P-Values. Parkland College. Isela Guerra Parkland College. Recommended Citation Parkland College A with Honors Projects Honors Program 2014 Calculating P-Values Isela Guerra Parkland College Recommended Citation Guerra, Isela, "Calculating P-Values" (2014). A with Honors Projects.

### Stat 5102 Notes: Nonparametric Tests and. confidence interval Stat 510 Notes: Nonparametric Tests and Confidence Intervals Charles J. Geyer April 13, 003 This handout gives a brief introduction to nonparametrics, which is what you do when you don t believe the assumptions

### Multiple-Comparison Procedures Multiple-Comparison Procedures References A good review of many methods for both parametric and nonparametric multiple comparisons, planned and unplanned, and with some discussion of the philosophical

### Guide to Microsoft Excel for calculations, statistics, and plotting data Page 1/47 Guide to Microsoft Excel for calculations, statistics, and plotting data Topic Page A. Writing equations and text 2 1. Writing equations with mathematical operations 2 2. Writing equations with

### MINITAB ASSISTANT WHITE PAPER MINITAB ASSISTANT WHITE PAPER This paper explains the research conducted by Minitab statisticians to develop the methods and data checks used in the Assistant in Minitab 17 Statistical Software. One-Way

### II. DISTRIBUTIONS distribution normal distribution. standard scores Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,

### Chapter 7 Section 7.1: Inference for the Mean of a Population Chapter 7 Section 7.1: Inference for the Mean of a Population Now let s look at a similar situation Take an SRS of size n Normal Population : N(, ). Both and are unknown parameters. Unlike what we used

### Statistics Review PSY379 Statistics Review PSY379 Basic concepts Measurement scales Populations vs. samples Continuous vs. discrete variable Independent vs. dependent variable Descriptive vs. inferential stats Common analyses

### Chapter 3 RANDOM VARIATE GENERATION Chapter 3 RANDOM VARIATE GENERATION In order to do a Monte Carlo simulation either by hand or by computer, techniques must be developed for generating values of random variables having known distributions.

### C. The null hypothesis is not rejected when the alternative hypothesis is true. A. population parameters. Sample Multiple Choice Questions for the material since Midterm 2. Sample questions from Midterms and 2 are also representative of questions that may appear on the final exam.. A randomly selected sample

### Chapter 5 Analysis of variance SPSS Analysis of variance Chapter 5 Analysis of variance SPSS Analysis of variance Data file used: gss.sav How to get there: Analyze Compare Means One-way ANOVA To test the null hypothesis that several population means are equal, PholC60 September 001 DATA INTERPRETATION AND STATISTICS Books A easy and systematic introductory text is Essentials of Medical Statistics by Betty Kirkwood, published by Blackwell at about 14. DESCRIPTIVE Analysis of Variance ANOVA Overview We ve used the t -test to compare the means from two independent groups. Now we ve come to the final topic of the course: how to compare means from more than two populations.