# Calculate Confidence Intervals Using the TI Graphing Calculator

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1 Calculate Confidence Intervals Using the TI Graphing Calculator Confidence Interval for Population Proportion p Confidence Interval for Population μ (σ is known 1 Select: STAT / TESTS / 1-PropZInt x: number of successes found in sample C-Level: 090, 095, 099, etc Confidence Interval: (lower bound, upper bound Calculated value of p-hat statistic Sample size n Confidence Interval Population μ (σ is unknown Select: STAT / TESTS / TInterval x : value of the sample mean statistic sx: value of sample standard deviation statistic C-Level: 090, 095, 099, etc Output: Confidence Interval: (lower bound, upper bound Value of the sample x-bar statistic Value of the standard deviation statistic s Sample size n CI for Difference of Population Proportions ( p 1 p 2 Select: STAT / TESTS / 2-PropZInt x1: number of successes found in first sample n1 : sample size of first sample x2: number of successes found in second sample n2 : sample size of second sample C-Level: 090, 095, 099, etc Confidence Interval: (lower bound, upper bound Calculated value of p-hat statistic of first sample Calculated value of p-hat statistic of second sample Sample size n of first sample Sample size n of second sample Select: STAT / TESTS / ZInterval σ : value of population standard deviation x: value of sample mean statistic C-Level: 090, 095, 099, etc Confidence Interval: (lower bound, upper bound Value of x-bar statistic Sample size n Confidence Interval Population σ The current versions of the TI graphing calculators do not have a program to calculate confidence intervals for σ 2 and σ There is a program named S2INT that can be installed on a TI See page 383 of your text book for details CI for Difference of Population Means ( μ 1 μ 2 Population σ's are unknown, but we assume σ 1 = σ 2 Sample statistics are taken from two independent simple random samples Select: STAT / TESTS/ 2-SampTInt x1: sample mean statistic of first sample sx1 : sample standard deviation of first sample n1 : sample size of first sample x2: sample mean statistic of second sample sx2 : sample standard deviation of second sample n2 : sample size of second sample C-Level: 090, 095, 099, etc Pooled: Select Yes (We are assuming σ 1 = σ 2 Confidence Interval: (lower bound, upper bound Degrees of freedom used for t-distribution Mean of first sample Mean of second sample Standard deviation of first sample Standard deviation of second sample Pooled sample standard deviation Sample size of first sample Sample size of second sample

2 Hypothesis Tests Using the TI Graphing Calculator (pages Hypothesis Test for Population Proportion p Select: STAT / TESTS / 1-PropZTest po : the population proportion stated in H o x: number of successes found in sample prop po < po > po (select Value of z-standard normal dist test statistic Calculated value of p-hat statistic Size of random sample Hypothesis Test for Population μ ( σ known Select: STAT / TESTS / Z-Test μo : the population μ stated in H o σ = the standard deviation of the parent pop x : the sample mean statistic μ μo < μo > μo (select Value of z-standard normal distribution test statistic Value of sample mean statistic Size of random sample Hypothesis Test for Population μ ( σ unknown Select: STAT / TESTS / T-Test μo : the population μ stated in H o x : the sample mean statistic Sx : the sample standard deviation statistic μ μo < μo > μo (select Value of t-distribution test statistic Value of sample mean statistic Value of the sample standard deviation statistic Size of random sample Test for Difference of Population p's ( p 1 - p 2 Select: STAT / TESTS / 2-PropZTest x1: the number of successes in first sample n1: size of first sample x2: the number of successes in the second sample n2: sample size of the second sample p1 p2 < p2 > p2 (select Value of z-standard normal distribution test statistic Calculated p-hat of first sample Calculated p-hat of second sample Calculated pooled p-hat statistic of two samples Size of first random sample Size of second random sample

3 3 Test for Difference of Population μ's ( μ 1 - μ 2 Population σ's are unknown and we do not assume σ 1 = σ 2 Sample statistics are taken from two independent simple random samples If the parent populations are not normal, the sample sizes should be 30 or more Select: STAT / TESTS / 2-SampTTest x1: sample mean of first sample Sx1: sample standard deviation of first sample n1: sample size of first sample x2: sample mean of second sample Sx2 : sample standard deviation of second sample n2: sample size of the second sample μ1 μ2 < μ2 > μ2 (select Pooled: Select No (We are not assuming σ 1 = σ 2 Value of t-distribution test statistic Degrees of freedom of the t-distribution Sample mean of first sample Sample mean of second sample Sample standard deviation of first sample Sample standard deviation of second sample Size of first random sample Size of second random sample Test for Mean of Paired Differences of Two Dependent Populations - d Matched data pairs are taken from two dependent simple random samples of equal size The random variable d is a difference statistic where the i th difference d i = x i y i where x i is the i th data value from the first sample and y i is the i th data value from the second sample The number of matched pairs should be > 30 or the difference values should come from a normal or almost normal population Preliminary Steps: a Store the x-values from the first sample in L 1 b Store the y-values from the second sample in L 2 c Store the list of differences L 1 - L 2 in L 3 Clear the screen and enter the following command: L 1 L 2 L 3 STO> button located near lower-left region of keypad (The d-bar statistic has a t-distribution Select: STAT / TESTS/ T-Test Inpt: Use arrow keys select Data μ0 : 0 (Ho states that the mean of differences = 0 List : L3 Freq: 1 μ μ0 < μ0 > μ0 (select Value of t-distribution test statistic Sample mean of the differences in L3 Sample standard deviation of the differences in L3 n = number of data values in L3

4 Goodness-of-Fit Hypothesis Test Test whether or not the observed frequencies of a set of data values have a particular probability distribution Each data set is partitioned into k categories so that one set contains the observed frequency of each category and the other data set contains the expected frequency of each category This test involves calculating the value of a chi-square test statistic and then determine whether or not the value of the χ 2 test statistic deviates too far to the right-tail of the χ 2 pd curve Large test statistic values indicate that the observed data frequencies deviate too far from the expected frequencies and therefore the observed frequencies do not fit a particular probability distribution Preliminary Steps: a Store the k observed frequencies in L1 b Store the k expected frequencies in L2 Select: STAT / TESTS / χ 2 GOF-Test Observed : L1 Expected : L2 df : degrees of freedom = number of categories - 1 χ 2 = value of the chi-square test statistic df = degrees of freedom Hypothesis Test for Homogeneity Test to see if two or more populations have the same proportions of different characteristics of interest The test involves two contingency tables and a χ 2 test statistic Each row of a table contains the population frequencies which correspond to the proportions of a population The cell values in row r of one table are the observed population characteristic frequencies of population r The cell values in row r of the second table are the expected population characteristic frequencies of population r Follow the same procedure for doing a hypothesis test for independence which is described above Hypothesis Test for Independence Test whether or not events in a sample space are independent The intersection of two events in the sample space corresponds to the intersection of a row and column of a contingency table Hence the sample space is partitioned into r disjoint events which correspond to the r rows of the contingency table The c columns of the contingency table correspond to a second partition of the sample space into c disjoint events There are two contingency tables; one table contains observed frequencies and the other table contains expected frequencies The test involves calculating the value of a χ 2 test statistic and then determine whether or not the value of the test statistic deviates too far to the right-tail of the χ 2 pd curve Large right-tail χ 2 test values indicate that the observed data frequencies deviate too far from the expected frequencies and therefore the events in the sample space are not independent The df parameter of the χ 2 pd = (r 1(c 1 where r and c equal the number of rows and columns of a contingency table For the test to be valid, the expected frequency of every cell in the expected contingency table must be > 5 Preliminary Steps: Use the TI MATRIX-EDIT menu command to create an r-row by c-column matrix of observed frequencies Enter and save the observed frequencies in matrix [A] Use the TI MATRIX-EDIT menu command to create an r-row by c-column matrix [B] which will contain the expected frequencies Do not bother to fill in the cells of [B] since [B] cell values will be automatically calculated and filled in later Select: STAT / TESTS / χ 2 -Test Observed : [A] Expected : [B] χ 2 = value of the chi-square test statistic [B] will now contain the expected frequencies 4

5 Find Equation of Regression Line (y = a + bx, Sample Correlation Coefficient r and the Coefficient of Determination r 2 with the TI 83/84 + graphing calculator a Clear lists L 1 and L 2 b Enter the x-coordinates in list L 1 c Enter the y-coordinates in list L 2 d Press STAT / TESTS / LinRegTTest Xlist : L 1 Linear Regression and Correlation Calculations 5 Linear Correlation Hypothesis Test H 0 : ρ = 0 - There is no linear correlation : ρ 0 - There is linear correlation Ylist : L 2 Freq : 1 β and ρ : 0 RegEQ : Select Calculate and press the ENTER key Probability distribution of the test statistic t = r / ( (1 r 2 / (n 2 is a t-distribution with n-2 degrees of freedom y = a + bx β 0 and ρ 0 β (beta is a population parameter equal to the true value of the slope of the regression line ρ (rho is a population parameter equal to the true value of the correlation coefficient t = value of test statistic derived from a random sample Be sure to record the values of a and b so p = that you can graph the regression line in the degrees of freedom of t-distribution = n - 2 future Also record the values of r and r 2 a = y-intercept of the regression line b = slope of the regression line s = standard error where larger values of s indicate increased scattering of points r 2 = the coefficient of determination r = the sample correlation coefficient Draw Scatter Plot and Graph Regression Line with TI 83/84+ a Enter the x-coordinates in list L 1 b Enter the y-coordinates in list L 2 c Press the MODE button Select NORMAL number display mode Select FLOAT and set rounding to 4 decimal places Select FUNC graph type Select CONNECTED plot type Select SEQUENTIAL Select REAL number mode Select FULL screen mode d Press the WINDOW button Set axes scale values (Xmin, Xmax, etc to fit scatter plot data e Press the STAT PLOT key ( 2ND and Y= Set Plot 1 to on and all other plots to off Type : Select scatter plot icon (top-row-left Xlist : L 1 Ylist : L 2 Mark : Select desired style of plot marker f Press the Y= button Clear out all equations with the CLEAR key \Y 1 = equation of regression line: bx + a or a + bx g Press the GRAPH button to view the graph of the regression line After the least squares regression line is graphed, points on the regression line can be found as follows: Press the CALC (2ND TRACE key and select value Then enter a value for the x variable and press the ENTER key Continue entering other values of x as desired

6 Normal Quantile Plot Check to see if a sample of n data points came from a normal population 7 Enter the sample data values in list L 1 Press the STAT PLOT key ( 2ND and Y= keys Turn Plot1 on and the other plots to off Type : Select the plot icon in row-2-right If the quantile plot is linear or almost linear, the data values most likely came from a normal population If the quantile plot is not linear, the parent population is probably not normal Data List : L 1 Data Axis : X Mark : Select the desired data marker style Press the ZOOM key and 9 to generate a quantile plot of the sample data values Press the TRACE key to view the x-y coordinates of points on the graph Warning! Quantile plots of sample data taken from a uniform distribution may appear to be somewhat linear, however, the plot follows a systematic curved pattern about a straight line and therefore it is not considered to be a linear plot Example 1: The data set below is a random sample of 16 data values taken from an exponential population with μ = 4 and σ = 4 Exponential populations are very skewed to the right and therefore normal quantile plots of samples taken from an exponential population should not be linear { 0160, 7697, 0552, 2266, 2469, 5254, 8143, 2211, 2346, 3901, 31619, 3105, 11821, 0737, 1415, } Example 2: The data set below is a random sample of 21 data values taken from a normal population with μ = 100 and σ = 16 Since the sample was taken from a normal population, the normal quantile plot of the data set should be linear or almost linear See the comment below { 11327, 10732, 6768, 12248, 11457, 8715, 9044, 9949, 12036, 10338, 9926, 11060, 8892, 11899, 9263, 10133, 11660, 11019, 9820, 9679, } z-score of the percentile rank of x z-score of the percentile rank of x Comment: The n data values, { x i }, are first sorted in ascending order Each x i is assigned a percentile rank P i = ( i 05 / n Each y-coordinate y i = the z-score corresponding to P i = invnorm(p i, 0, 1 You can judge the straightness of a line by eye Do not pay too much attention to points at the ends of the plot, unless they are quite far from the line It is common for a few points at either end to stray from the line somewhat However, a point that is very far from the line when the other points are very close is an outlier, and deserves attention The Ryan-Joiner test is one of several other tests available to test for normality

7 6 Residual Plot Plot the residuals of a sample of n (x, y data points associated with the least-squares regression line of the n data points The residual of a data point (x,y equals the difference between y and y p = y y p where y p equals the y-value predicted by the least-squares regression line A residual equals the unexplained deviation between y and y p The least-squares regression line minimizes the sum of the squares of the residuals If the residual plot shows a random pattern about the x-axis, the least-squares regression line is a good model to fit the data If the residual plot pattern about the x-axis is not random, a nonlinear model such as quadratic or cubic model might be a more appropriate model to fit the data Select STAT / SetUpEditor and press the ENTER key Clear L 1, L 2 and L 3 Enter the x-coordinates in L 1 Enter the y-coordinates in L 2 Now create a special list for the residuals in the list editor Select STAT / EDIT Move the edit cursor to the very top of L 3 so that L 3 is highlighted Press the INS key ( 2 nd DEL = the insert key Use the command LIST / NAMES and select RESID from the list and then press the ENTER key to paste in the RESID symbol at the top of the new list Next calculate the equation of the least-squares regression line and correlation coefficient r Select STAT / TESTS / LinRegTTest Xlist: L 1 This is an important step if you intend to find the equation of the regression line or the correlation coefficient r at a later time Ylist: L 2 Freq: 1 β and ρ : 0 RegEQ : Select Calculate and press the ENTER key Record the y-intercept a, slope b, r and r 2 Select STAT / EDIT to view the residuals which were automatically inserted in list RESID Now plot the residuals for each x-y data point Press the STAT PLOT key ( 2ND and Y= keys Turn Plot1 on and the other plots to off Type : Select the scatter plot icon top-row-left Xlist : L 1 Ylist : RESID ( Select LIST / NAMES and then select RESID Mark : Select the desired data marker style Select ZOOM and 9 to generate a residual plot of the sample x-y data pairs Press the TRACE key to view the x-y coordinates of points on the residual plot Comment: Page 225 of the course text book has two excellent examples that illustrate how to use a residual plot to determine whether or not a linear model is an appropriate model of the data set

8 Construct a Frequency Histogram of a Data Set Set the variable n equal to the number of classes you wish to have Use STAT button to clear list L 1, enter the data values in list L 1 and then sort the data values in ascending order 8 Use STAT / EDIT to view the data values in L 1 Then find X min and X max and set the class width CW = (X max X min / n Always round CW up to the next whole number even if CW was a whole number Example: If CW = 9, round up to 10 Follow the procedure given in a separate handout to calculate class limits, class midpoints and class boundaries Press the STAT PLOT button (2 ND and Y = buttons Select Plot 1 Set Plot 1 to ON Type : Select the histogram icon in top-row-right (3 rd icon in list of icons Xlist: L1 Freq: 1 Mark : Select desired data graph marker A dot marker is difficult to see Make sure that all of the other STAT PLOTS are set to OFF Press the Y= button and use the CLEAR button to clear out any equation formulas Press the WINDOW button Set Xmin = the lower class boundary of the first class = the midpoint of first class - CW / 2 Set Xmax = the upper class boundary of the last class = the midpoint of the last class + CW / 2 Set Xscl = the class width = CW Set Ymin = -1 Set Ymax = a little more than the estimated maximum class frequency You can change this later if needed Set Yscl = 2 or 5 Too many ticks marks will make the y-axis too crowded Set Xres = 1 which is the highest possible screen resolution Press the GRAPH button to view the frequency histogram graph Press the TRACE button to view class boundaries and class frequencies Use the arrow keys to move from class to class Construct a Frequency Polygon of a Data Set Set the variable n equal to the number of classes you wish to have Find the class midpoints and class frequencies Refer to the procedure above for calculating a frequency histogram Let Mn = the midpoint of the nth class and Fn = the frequency of the nth class In list L 2 enter the n + 2 x-coordinates of the polygon graph : M1- CW, M1, M2, M3,, Mn, Mn + CW In list L 3 enter the n + 2 y-coordinates of the polygon graph : 0, F1, F2, F3,, Fn, 0 Press the STAT PLOT button (2 ND and Y = buttons Select Plot 1 Set Plot 1 to ON Type : Select polygon frequency icon in middle of top-row (2 nd icon in list of icons Xlist : L 2 Ylist : L 3 Mark : Select desired data graph marker A dot marker is difficult to see Make sure that all of the other STAT PLOTS are set to OFF Press the Y= button and use the CLEAR button to clear out any equation formulas Press the ZOOM button and 9 to view the frequency polygon graph Press the TRACE button to view class midpoints and class frequencies Use the arrow keys to move from class to class Construct a Box-and-Whisker Plot of a Data Set In list L 1 enter the numbers in the data set Press STAT PLOT button and set Plot 1 to ON and all other plots to OFF Set the following plot settings: Xlist : L 1, Type : Select desired box-and-whisker icon, Freq : 1, Mark : Select a graph marker Press the ZOOM button and 9 to view to the graph of the box-and-whisker plot Press the TRACE button to view the key plot points Use the arrow keys to move from point to point One of the box-plot icons is for a plot that does not connect the whiskers to outliers This makes it easy to identify outliers

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