hown below is a portion of a computer output for regression analysis relating y (dependent variable) and x (independent variable). ANOVA df SS Regression 1 24.061 Residual 10 67.979 Coefficients Standard Error Intercept 11.064 2.049 x −0.566 0.301 (a) What has been the sample size for the above regression analysis?
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ANOVA
df | SS | |
---|---|---|
Regression | 1 | 24.061 |
Residual | 10 | 67.979 |
Coefficients | Standard Error | |
---|---|---|
Intercept | 11.064 | 2.049 |
x |
−0.566
|
0.301 |
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- We are interested in the relationship between mid-term exam scores and final exam scores. The Final Exam score is the dependent variable and Midterm score is the independent variable. Use the simple regression output on below to answer the question below. Use a significance level of 0.05 for all hypothesis tests and intervals. What was the sample size for this simple regression analysis? Analysis of Variance Sum of Source DF Squares Mean Square F Ratio Model 1 2632.8012 2632.80 23.2115 Error 54 6125.0381 113.43 Prob > F C. Total 55 8757.8393 |t| Lower 95% Upper 95% Intercept 31.738559 10.26099 Midterm 3.09 0.0031* 11.166522 52.310596 0.8768157 0.61916 0.128514 4.82 <.0001* 0.3615044A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on the employee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 Predict the sales next month for an employee with 2.5 years of experience. The predicted sales is _________ cars. (Type an integer or decimal rounded to one decimal place as needed.)A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on the employee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 The predicted sales is 8.6 cars. Compute the coefficient of determination and interpret its meaning. The coefficient of determination is (Type an integer or decimal rounded to three decimal places as needed.)
- A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 0.203521 The coefficient of determination is 0.234 Test statistic= 0.704 P-value= 0.014 Construct a 95% confidence interval around the sample slope and interpret its meaning. The confidence interval is (__________,_________). (Type an integer or decimal rounded to three decimal places as needed.)Shown below is a portion of a computer output for regression analysis relating to Y (dependent variable) and X (independent variable). ANOVA df SS Regression 1 24.011 Residual 8 67.989 Coefficients Standard Error Intercept 11.065 2.043 x -0.511 0.304 What has been the sample size of the above? Perform a t test and determine whether or not X and Y are related. Let level of significance= 0.05 Perform an F test and determine whether or not X and Y are related. Let level of significance=0.05 Compute the coefficient of determination. Interpret the meaning of the value of the coefficient of determination that you found in (4). Be specific.A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 1. Predict the sales next month for an employee with 2.5 years of experience. The predicted sales is 8.6 cars. 2. Compute the coefficient of determination and interpret its meaning. The coefficient of determination is 0.234. 3. Do the sample data provide evidence that the model is useful for predicting average monthly sales for employees based on their sales experience using α=0.05? The test statistic is (Type an integer or decimal rounded to two decimal places as…
- A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 0.203521 The predicted sales is 8.6 cars. The coefficient of determination is 0.234 Test Statistic is 7.04 P-value=We are interested in the relationship between mid-term exam scores and final exam scores. The Final Exam score is the dependent variable and Midterm score is the independent variable. Use the simple regression output on below to answer the question below. Use a significance level of 0.05 for all hypothesis tests and intervals. What proportion of the variation in the Final Exam scores is not "explained" by the simple linear relationship with Midterm score? Analysis of Variance Sum of Source DF Squares Mean Square F Ratio Model 1 2632.8012 2632.80 23.2115 Error 54 6125.0381 113.43 Prob > F C. Total 55 8757.8393 It| Lower 95% Upper 95% Intercept 31.738559 10.26099 Midterm 3.09 0.0031* 11.166522 52.310596 0.61916 0.128514 4.82 <.0001* 0.3615044 0.8768157Brain size Does your IQ depend on the size of yourbrain? A group of female college students took a test thatmeasured their verbal IQs and also underwent an MRI scan to measure the size of their brains (in 1000s of pix-els). The scatterplot and regression analysis are shown, and the assumptions for inference were satisfied.Dependent variable is: IQ_VerbalR-squared = 6.5% s = 21.5291 df = 18Variable Coefficient SE(Coeff)Intercept 24.1835 76.38Size 0.098842 0.0884a) Test an appropriate hypothesis about the associationbetween brain size and IQ.b) State your conclusion about the strength of thisassociation.
- We are interested in the relationship between mid-term exam scores and final exam scores. The Final Exam score is the dependent variable and Midterm score is the independent variable. Use the simple regression output on below to answer the question below. Use a significance level of 0.05 for all hypothesis tests and intervals. A point estimate for the Final Exam score for a student with a score of 85 on the Midterm? O Bivariate Fit of Final Exam By Midterm 100 90 80 70 60 50 55 60 65 70 75 80 85 90 95 100 Midterm Parameter Estimates Term Estimate Std Error t Ratio Prob>|t| Lower 95% Upper 95% 0.0031* <.0001* 11.166522 0.3615044 3.09 Intercept 31.738559 10.26099 Midterm 52.310596 0.61916 0.128514 4.82 0.8768157 Upper 95% Indiv Final ... Predicted Lower 95% Upper 95% Lower 95% Student Midterm Final Exam Final Exam Mean Final ... Mean Final .. Indiv Final ... 70 75.07976245 71.391337991 78.768186908 53.411179985 96.748344914 Final ExamA real estate builder wishes to determine how house size (House) is influenced by family income (Income) and family size (Size). House size is measured in meter square and income is measured in IDR millions. The builder randomly selected 50 families and ran the multiple regression. Partial Microsoft Excel output is provided below: What is the sample estimates of the regression problem? Which of the independent variables in the model are significant at the 5% level? Formulate the hypothesis and explain the answer.The average height of a large group of children is 43 inches, and the SD is 1.2inches. The average weight of these children is 40 pounds, and the SD is 2pounds. The correlation between the two variables is r = 0.65.A scatter diagram is drawn, with height on the horizontal axis and weight on thevertical axis. The scatter diagram is football shaped. The regression line forpredicting weight based on height is drawn through the scatter.(a) Predict the weights and the typical size of the error for those predictions ineach of the following case:A child who is 43 inches tall is predicted to weigh _____________ pounds, give ortake _____________ pounds.A child who is 41.8 inches tall is predicted to weigh ____________ pounds, give ortake _____________ pounds.