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... of a list. 2: max( Returns maximum element of a list. 3: mean( Returns mean value of a list. median( returns the median value of elements in list. 7: stdDev( Returns standard deviation of a list. 8: variance( Returns the variance of list.
... of a list. 2: max( Returns maximum element of a list. 3: mean( Returns mean value of a list. median( returns the median value of elements in list. 7: stdDev( Returns standard deviation of a list. 8: variance( Returns the variance of list.
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... can combine sum( or prod( with seq( to obtain: upper G expression(x) upper ∏ expression(x) x=lower x=lower To evaluate G 2(N-1) from N=1 to 4: stdDev(, variance( stdDev( returns the standard deviation of elements. Chapter 11: Lists 176 start and end elements are optional; they specify a range of list. prod( returns the product of all elements of...
... can combine sum( or prod( with seq( to obtain: upper G expression(x) upper ∏ expression(x) x=lower x=lower To evaluate G 2(N-1) from N=1 to 4: stdDev(, variance( stdDev( returns the standard deviation of elements. Chapter 11: Lists 176 start and end elements are optional; they specify a range of list. prod( returns the product of all elements of...
Guidebook
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... variables are calculated and stored as Y4 and then press Í. y minimum of x values maximum of x values minimum of y values maximum of x ... If you to finish the Manual Fit function. To access these variables for graphing. Variables mean of x values sum of x values sum of x2 values sample standard deviation of x population standard deviation of x number...
... variables are calculated and stored as Y4 and then press Í. y minimum of x values maximum of x values minimum of y values maximum of x ... If you to finish the Manual Fit function. To access these variables for graphing. Variables mean of x values sum of x values sum of x2 values sample standard deviation of x population standard deviation of x number...
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... height values below . Press Í. 3. Press ... Í to be normally distributed, a t distribution confidence interval can be used when estimating the mean of 165.1 centimeters and a standard deviation of 6.35 centimeters (randNorm(165.1,6.35,90) with a seed of the list. The Ø cursor indicates that alpha-lock is displayed on . Enter [H] [G] [H] [T] at the...
... height values below . Press Í. 3. Press ... Í to be normally distributed, a t distribution confidence interval can be used when estimating the mean of 165.1 centimeters and a standard deviation of 6.35 centimeters (randNorm(165.1,6.35,90) with a seed of the list. The Ø cursor indicates that alpha-lock is displayed on . Enter [H] [G] [H] [T] at the...
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Page 223
...about 159.74 centimeters and 173.94 centimeters. The editor changes so that in the calculated interval. This is between about a 14.2 centimeters spread. The third line gives the sample standard deviation Sx. The inferential stat editor for TInterval. If Data is not selected for the population... select 8:TInterval. The second line gives the mean v of 163.8 and sample standard deviation Sx of the sample v used to display the STAT TESTS menu, and then press † until HGHT is calculated, and the TInterval results are displayed on the population mean of women's heights, ...
...about 159.74 centimeters and 173.94 centimeters. The editor changes so that in the calculated interval. This is between about a 14.2 centimeters spread. The third line gives the sample standard deviation Sx. The inferential stat editor for TInterval. If Data is not selected for the population... select 8:TInterval. The second line gives the mean v of 163.8 and sample standard deviation Sx of the sample v used to display the STAT TESTS menu, and then press † until HGHT is calculated, and the TInterval results are displayed on the population mean of women's heights, ...
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... v. Press † 163 Ë 8 Í to store 163.8 to these values. Press † to move the cursor onto Calculate, and then press Í to calculate the result. Enter the information as follows: Press Ë 95 †165 Ë 1 † 6 Ë 35 †...calculate the new 99 percent confidence interval. Now graph and shade the top 5 percent of the women (the 95th percentile)? 4. Press 7 Ë 1 Í to store 7.1 to clear the home screen. If the height distribution among a population of women is normally distributed with a mean m of 165.1 centimeters and a standard deviation...
... v. Press † 163 Ë 8 Í to store 163.8 to these values. Press † to move the cursor onto Calculate, and then press Í to calculate the result. Enter the information as follows: Press Ë 95 †165 Ë 1 † 6 Ë 35 †...calculate the new 99 percent confidence interval. Now graph and shade the top 5 percent of the women (the 95th percentile)? 4. Press 7 Ë 1 Í to store 7.1 to clear the home screen. If the height distribution among a population of women is normally distributed with a mean m of 165.1 centimeters and a standard deviation...
Guidebook
Page 225
Enter 175 Ë 5448205 for the upper bound and press †. Enter a standard deviation s of the ShadeNorm( parameters. 10. low is displayed. Inferential Stat Editors Displaying the Inferential Stat Editors When you select the ANOVA( instruction, it is the ...
Enter 175 Ë 5448205 for the upper bound and press †. Enter a standard deviation s of the ShadeNorm( parameters. 10. low is displayed. Inferential Stat Editors Displaying the Inferential Stat Editors When you select the ANOVA( instruction, it is the ...
Guidebook
Page 230
... population standard deviation s is known. It tests the null hypothesis H0: m=m0 against one -sample z test; Chapter 13: Inferential Statistics and Distributions 223 Z-Test Z-Test (one of the alternatives below. • Ha: mƒm0 (m:ƒm0) • Ha: mm0) In the example: L1={299.4, 297.7, 301, 298.9, 300.2, 297} Data Input: Stats Calculated...
... population standard deviation s is known. It tests the null hypothesis H0: m=m0 against one -sample z test; Chapter 13: Inferential Statistics and Distributions 223 Z-Test Z-Test (one of the alternatives below. • Ha: mƒm0 (m:ƒm0) • Ha: mm0) In the example: L1={299.4, 297.7, 301, 298.9, 300.2, 297} Data Input: Stats Calculated...
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It tests the null hypothesis H0: m=m0 against one -sample t test; T-Test T-Test (one of the alternatives below. • Ha: mƒm0 (m:ƒm0) • Ha: mm0) In the example: TEST={91.9, 97.8, 111.4, 122.3, 105.4, 95} Data Input: Stats Calculated results: Drawn results: Chapter 13: Inferential Statistics and Distributions 224 item 2) performs a hypothesis test for a single unknown population mean m when the population standard deviation s is unknown.
It tests the null hypothesis H0: m=m0 against one -sample t test; T-Test T-Test (one of the alternatives below. • Ha: mƒm0 (m:ƒm0) • Ha: mm0) In the example: TEST={91.9, 97.8, 111.4, 122.3, 105.4, 95} Data Input: Stats Calculated results: Drawn results: Chapter 13: Inferential Statistics and Distributions 224 item 2) performs a hypothesis test for a single unknown population mean m when the population standard deviation s is unknown.
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item 3) tests the equality of the means of the alternatives below. • Ha: m1ƒm2 (m1:ƒm2) • Ha: m1m2) In the example: LISTA={154, 109, 137, 115, 140} LISTB={108, 115, 126, 92, 146} Data Input: Stats Calculated results: Drawn results: Chapter 13: Inferential Statistics and Distributions 225 The null hypothesis H0: m1=m2 is tested against one of two populations (m1 and m2) based on independent samples when both population standard deviations (s1 and s2) are known. 2-SampZTest 2-SampZTest (two-sample z test;
item 3) tests the equality of the means of the alternatives below. • Ha: m1ƒm2 (m1:ƒm2) • Ha: m1m2) In the example: LISTA={154, 109, 137, 115, 140} LISTB={108, 115, 126, 92, 146} Data Input: Stats Calculated results: Drawn results: Chapter 13: Inferential Statistics and Distributions 225 The null hypothesis H0: m1=m2 is tested against one of two populations (m1 and m2) based on independent samples when both population standard deviations (s1 and s2) are known. 2-SampZTest 2-SampZTest (two-sample z test;
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2-SampTTest 2-SampTTest (two-sample t test; item 4) tests the equality of the means of the alternatives below. • Ha: m1ƒm2 (m1:ƒm2) • Ha: m1m2) In the example: SAMP1={12.207, 16.869, 25.05, 22.429, 8.456, 10.589} SAMP2={11.074, 9.686, 12.064, 9.351, 8.182, 6.642} Data Input: Stats Calculated results: Drawn results: Chapter 13: Inferential Statistics and Distributions 226 The null hypothesis H0: m1=m2 is tested against one of two populations (m1 and m2) based on independent samples when neither population standard deviation (s1 or s2) is known.
2-SampTTest 2-SampTTest (two-sample t test; item 4) tests the equality of the means of the alternatives below. • Ha: m1ƒm2 (m1:ƒm2) • Ha: m1m2) In the example: SAMP1={12.207, 16.869, 25.05, 22.429, 8.456, 10.589} SAMP2={11.074, 9.686, 12.064, 9.351, 8.182, 6.642} Data Input: Stats Calculated results: Drawn results: Chapter 13: Inferential Statistics and Distributions 226 The null hypothesis H0: m1=m2 is tested against one of two populations (m1 and m2) based on independent samples when neither population standard deviation (s1 or s2) is known.
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Page 236
... 8) computes a confidence interval for an unknown population mean m when the population standard deviation s is known. In the example: L6={1.6, 1.7, 1.8, 1.9} Data Input: Stats Chapter 13: Inferential Statistics and Distributions 229 In the example: L1={299.4, 297.7, 301, 298.9, 300.2, 297} Data Input: Stats Calculated results: TInterval TInterval (one -sample z confidence interval; ZInterval ZInterval (one...
... 8) computes a confidence interval for an unknown population mean m when the population standard deviation s is known. In the example: L6={1.6, 1.7, 1.8, 1.9} Data Input: Stats Chapter 13: Inferential Statistics and Distributions 229 In the example: L1={299.4, 297.7, 301, 298.9, 300.2, 297} Data Input: Stats Calculated results: TInterval TInterval (one -sample z confidence interval; ZInterval ZInterval (one...
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item 9) computes a confidence interval for the difference between two population means (m1Nm2) when both population standard deviations (s1 and s2) are known. In the example: LISTC={154, 109, 137, 115, 140} LISTD={108, 115, 126, 92, 146} Data Input: Stats Calculated results: Chapter 13: Inferential Statistics and Distributions 230 Data Calculated results: Stats 2-SampZInt 2-SampZInt (two-sample z confidence interval; The computed confidence interval depends on the user-specified confidence level.
item 9) computes a confidence interval for the difference between two population means (m1Nm2) when both population standard deviations (s1 and s2) are known. In the example: LISTC={154, 109, 137, 115, 140} LISTD={108, 115, 126, 92, 146} Data Input: Stats Calculated results: Chapter 13: Inferential Statistics and Distributions 230 Data Calculated results: Stats 2-SampZInt 2-SampZInt (two-sample z confidence interval; The computed confidence interval depends on the user-specified confidence level.
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2-SampTInt 2-SampTInt (two-sample t confidence interval; item 0) computes a confidence interval for the difference between two population means (m1Nm2) when both population standard deviations (s1 and s2) are unknown. The computed confidence interval depends on the user-specified confidence level. In the example: SAMP1={12.207, 16.869, 25.05, 22.429, 8.456, 10.589} SAMP2={11.074, 9.686, 12.064, 9.351, 8.182, 6.642} Data Input: Stats Calculated results: Chapter 13: Inferential Statistics and Distributions 231
2-SampTInt 2-SampTInt (two-sample t confidence interval; item 0) computes a confidence interval for the difference between two population means (m1Nm2) when both population standard deviations (s1 and s2) are unknown. The computed confidence interval depends on the user-specified confidence level. In the example: SAMP1={12.207, 16.869, 25.05, 22.429, 8.456, 10.589} SAMP2={11.074, 9.686, 12.064, 9.351, 8.182, 6.642} Data Input: Stats Calculated results: Chapter 13: Inferential Statistics and Distributions 231
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Page 242
item E) computes an Ü-test to compare two normal population standard deviations (s1 and s2). The population means and standard deviations are all unknown. 2-SampÜTest, which uses the ratio of sample variances Sx12/Sx22, tests the null hypothesis H0: s1=s2 against one of ...: s1s2) In the example: SAMP4={7,L4, 18, 17, L3, L5, 1, 10, 11, L2} SAMP5={L1, 12, L1, L3, 3, L5, 5, 2, L11, L1, L3} Data Input: Stats Calculated results: Drawn results: Chapter 13: Inferential Statistics and Distributions 235 2-SampFTest 2-SampÜTest (two-sample Ü-test;
item E) computes an Ü-test to compare two normal population standard deviations (s1 and s2). The population means and standard deviations are all unknown. 2-SampÜTest, which uses the ratio of sample variances Sx12/Sx22, tests the null hypothesis H0: s1=s2 against one of ...: s1s2) In the example: SAMP4={7,L4, 18, 17, L3, L5, 1, 10, 11, L2} SAMP5={L1, 12, L1, L3, 3, L5, 5, 2, L11, L1, L3} Data Input: Stats Calculated results: Drawn results: Chapter 13: Inferential Statistics and Distributions 235 2-SampFTest 2-SampÜTest (two-sample Ü-test;
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... | 0. No instructs the TI-84 Plus not to generate for sample one -sample tests and intervals. Must be a real number, such that you are testing. List The name of the list containing the data you are L1 and L2, respectively. Calculate/Draw Determines the type of ... 0. s The known population standard deviation; In tests, Draw draws a graph of successes from sample one for 2-SampTTest and 2-SampTInt. Defaults=1. x2 The count of the results. Must be a real number > 0. List1, List2 The names of output to pool the variances. Yes instructs the TI-84 Plus to be an integer | ...
... | 0. No instructs the TI-84 Plus not to generate for sample one -sample tests and intervals. Must be a real number, such that you are testing. List The name of the list containing the data you are L1 and L2, respectively. Calculate/Draw Determines the type of ... 0. s The known population standard deviation; In tests, Draw draws a graph of successes from sample one for 2-SampTTest and 2-SampTInt. Defaults=1. x2 The count of the results. Must be a real number > 0. List1, List2 The names of output to pool the variances. Yes instructs the TI-84 Plus to be an integer | ...
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Page 247
...integer > 0. Observed must be an integer > 0. The names of the lists containing the data for the 2-PropZTest and 2-PropZInt. Defaults are calculated as a percent and is divided by 100. The default is to store the regression equation to be at least 2×2. The count of observations ... expected values should be given as indicated below . Variables p-value test statistics degrees of freedom sample mean of x values for sample 1 and sample 2 sample standard deviation of x for sample 1 and sample 2 number of data points for the c2-Test and c2GOF-Test. Must be , 0 and < 100. The matrix...
...integer > 0. Observed must be an integer > 0. The names of the lists containing the data for the 2-PropZTest and 2-PropZInt. Defaults are calculated as a percent and is divided by 100. The default is to store the regression equation to be at least 2×2. The count of observations ... expected values should be given as indicated below . Variables p-value test statistics degrees of freedom sample mean of x values for sample 1 and sample 2 sample standard deviation of x for sample 1 and sample 2 number of data points for the c2-Test and c2GOF-Test. Must be , 0 and < 100. The matrix...
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...: Selection of any of the DISTR functions will take the user to a wizard screen for population 2 confidence interval pair mean of x values sample standard deviation of x number of data points standard error about the line regression/fit coefficients correlation coefficient coefficient of determination regression equation Tests ,Ç ,Ç1 ,Ç2 v Sx n Intervals ,Ç ,Ç...
...: Selection of any of the DISTR functions will take the user to a wizard screen for population 2 confidence interval pair mean of x values sample standard deviation of x number of data points standard error about the line regression/fit coefficients correlation coefficient coefficient of determination regression equation Tests ,Ç ,Ç1 ,Ç2 v Sx n Intervals ,Ç ,Ç...
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The defaults are m=0 and s=1. The defaults are mean m and standard deviation s. The probability density function (pdf) is: f(x) = ------1-------- --(---x 2 e 2σ2 ,σ > 0 2πσ normalpdf(x[,m,s]) Note: For this example, Xmin = ...for the normal distribution at a specified x value. normalpdf( normalpdf( computes the probability density function (pdf) for the specified mean m=0 and standard deviation s=1. normalcdf( normalcdf( computes the normal distribution probability between them, and then select 0:ZoomFit from the ZOOM menu. DISTR DRAW A: binompdf(...
The defaults are m=0 and s=1. The defaults are mean m and standard deviation s. The probability density function (pdf) is: f(x) = ------1-------- --(---x 2 e 2σ2 ,σ > 0 2πσ normalpdf(x[,m,s]) Note: For this example, Xmin = ...for the normal distribution at a specified x value. normalpdf( normalpdf( computes the probability density function (pdf) for the specified mean m=0 and standard deviation s=1. normalcdf( normalcdf( computes the normal distribution probability between them, and then select 0:ZoomFit from the ZOOM menu. DISTR DRAW A: binompdf(...
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...inverse cumulative Student-t probability function specified by Degree of Freedom, df for a given Area under the normal distribution curve specified by mean m and standard deviation s. df (degrees of the x value. 0 area 1 must be true. invT(area,df) tpdf( tpdf( computes... (pdf) is: f(x d----f---+-----1---)---/-2----] Γ(df ⁄ 2) (---1----+-----x---2---/--d---f--)------(--d---f--+----1---)--/-2πdf Chapter 13: Inferential Statistics and Distributions 243 It calculates the x value associated with an area to the Y= editor. The defaults are m=0 and s=1.
...inverse cumulative Student-t probability function specified by Degree of Freedom, df for a given Area under the normal distribution curve specified by mean m and standard deviation s. df (degrees of the x value. 0 area 1 must be true. invT(area,df) tpdf( tpdf( computes... (pdf) is: f(x d----f---+-----1---)---/-2----] Γ(df ⁄ 2) (---1----+-----x---2---/--d---f--)------(--d---f--+----1---)--/-2πdf Chapter 13: Inferential Statistics and Distributions 243 It calculates the x value associated with an area to the Y= editor. The defaults are m=0 and s=1.