### Testing the VariancesAssume both populations are normally distributed.#### Hypotheses- Null Hypothesis $ H_0: \sigma_1^2 \leq \sigma_2^2 $- Alternative Hypothesis $ H_A: \sigma_1^2 > \sigma_2^2 $ (Claim)#### Given Data- Sample variance of Population 1: $ s_1^2 = 5.6 $- Sample variance of Population 2: $ s_2^2 = 1.4 $- Sample size of Population 1: $ n_1 = 9 $- Sample size of Population 2: $ n_2 = 23 $- Significance level: $ \alpha = 0.10 $#### Two Sample F-Test for Variance| F-Test Statistic | Degrees of Freedom (Numerator) | Degrees of Freedom (Denominator) ||-----------------------------------------|----------------------------------------|----------------------------------------|| $ F = \frac{s_1^2}{s_2^2} $ | $ d_{f_N} = n_1 - 1 $ | $ d_{f_D} = n_2 - 1 $ || Where $ s_1^2 > s_2^2 $ | | |#### InstructionsRound all values to 3 decimal places.1. **Critical Value $ F_{ \alpha } $:** [___]2. **Test Statistic $ F $:** [___]3. **Conclusion:** [___]

Answered: a. The Critical Value F o = b. The Test…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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In ongoing economic analyses, the federal government compares per capita incomes not only among different states but also for the same state at different times. Typically, what the federal government finds is that "poor" states tend to stay poor and "wealthy" states tend to stay wealthy. Would we have gotten information about the 1999 per capita income for a state (denoted by y ) from its 1980 per capita income (denoted by x)? The following bivariate data give the per capita income (in thousands of dollars) for a sample of fifteen states in the years 1980 and 1999 (source: U.S. Bureau of Economic Analysis, Survey of Current Business, May 2000). The data are plotted in the scatter plot in Figure 1. Also given is the product of the 1980 per capita income and the 1999 per capita income for each of the fifteen states. (These products, written in the column labelled " xy ", may aid in calculations.) Question table 1980 per capita income, x (in $1000 s) 1999 per capita…
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In ongoing economic analyses, the federal government compares per capita incomes not only among different states but also for the same state at different times. Typically, what the federal government finds is that "poor" states tend to stay poor and "wealthy" states tend to stay wealthy. Would we have gotten information about the 1999 per capita income for a state (denoted by y) from its 1980 per capita income (denoted by x)? The following bivariate data give the per capita income (in thousands of dollars) for a sample of sixteen states in the years 1980 and 1999 (source: U.S. Bureau of Economic Analysis, Survey of Current Business, May 2000). The data are plotted in the scatter plot in Figure 1. Also given is the product of the 1980 per capita income and the 1999 per capita income for each of the sixteen states. (These products, written in the column labelled "xy", may aid in calculations.) Florida Nevada West Virginia Hawaii Arkansas Utah Kansas Montana North Dakota Nebraska…
A study was done to look at the relationship between number of lovers college students have had in their lifetimes and their GPAs. The results of the survey are shown below. Lovers 0 3 1 2 5 1 8 GPA 3.3 2.9 3.4 3.3 2.5 2.8 0.7 1. The p-value is: _____ (Round to four decimal places) 2. Use a level of significance of α=0.05 to state the conclusion of the hypothesis test in the context of the study (pick one). a. There is statistically significant evidence to conclude that there is a correlation between the number of lovers students have had in their lifetimes and their GPA. Thus, the regression line is useful. b. There is statistically significant evidence to conclude that a student who has had more lovers will have a lower GPA than a student who has had fewer lovers. c. There is statistically insignificant evidence to conclude that a student who has had more lovers will have a lower GPA than a student who has had fewer lovers. d. There is statistically insignificant…
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Managers of an outdoor coffee stand in Coast City are examining the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y , in dollars) and the maximum temperature (denoted by x , in degrees Fahrenheit) for each of fifteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1. Temperature, x(in degrees Fahrenheit) Coffee sales, y(in dollars) 53.1 2215.5 76.6 1982.7 70.1 1937.3 39.7 2251.1 72.5 1603.9 48.4 2024.2 46.7 2135.3 83.9 1536.5 58.4 1965.6 38.5 1944.3 68.2 1746.1 75.4 1472.1 65.6 1819.5 53.9 1627.4 45.1 1782.3 Send data to calculator Send data to Excel Coffee sales(in dollars) y 1200 1400 1600 1800 2000 2200 2400 x 40 50 60 70 80 90…
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Managers of an outdoor coffee stand in Coast City are examining the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y, in dollars) and the maximum temperature (denoted by x, in degrees Fahrenheit) for each of sixteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1. Temperature, x (in degrees Fahrenheit) Coffee sales, y (in dollars) 71.5 1970.9 58.7 1953.9 53.7 1791.1 2400+ 83.0 1570.3 2200+ 62.8 1852.7 74.6 1633.6 2000- ** 40.4 1973.9 1800- 51.5 2250.9 44.6 1808.5 1600- 45.5 1977.3 1400- 45.6 2190.5 1200 69.4 1789.1 55.0 1598.2 50 70 80 90 40.6 2272.0 74.0 1937.0 Figure 1 76.6 1547.1 Send data to Excel Continue Submit Assignment O 2021 McGraw-Hill Education. All Rights Reserved. Terms of Use Privacy Accessibility here to search 99+ 5 4.

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a. The Critical Value F o = b. The Test Statistic F = Corclusion