In trying to determine the relationship between consumer sentiment on annualized GDP growth, you collect data for 40 quarters, and regress GDP growth as the dependent variable on consumer sentiment as independent variable. The data statistics and regression results are as follows: GDP growth Sentiment 97 Mean 2.6 St.Dev. 2.9 Regression results: R-squared Standard error 0.51 0.56 Intercept 2.34 Intercept std. error 1.6 0.29 0.11 Slope Slope std. error What is the variance of the prediction error if you try to predict GDP growth when consumer sentiment 95, using the results of this regression? (Bonus thinking question: Is your prediction statistically different from zero at a 5% level of significance?)

We wish to determine if there is a correlation between the birth weight (in grams) of nine newborn infants and the length of their mothers’ stay (in days) in the hospital. The results of a correlation and regression analysis are indicated in the Excel output below. The mean birth weight of the newborn infants (the independent variable) was 3162.5 grams, and the mean length of their mothers’ stay in the hospital (the dependent variable) was 7 days. SUMMARY OUTPUT                                 Regression Statistics               Multiple R 0.862675               R Square 0.744208               Adjusted R Square 0.707666               Standard Error 6.02142               Observations 9                                 ANOVA                   df SS MS F Significance F       Regression 1 738.4198 738.4198 20.36599 0.002756       Residual 7 253.8025 36.25749           Total 8 992.2222…

In a study of the performance of a new engine design, the weight of 12 cars (in pounds) and the top speed (in mph) were recorded. A regression line was generated and shown to be an appropriate description of the relationship. The results of the regression analysis are below. Depend Variable: Top Speed Variable Constant Coefficient 107.58 s.e. of Coeff t-ratio prob Weight 0.8710 11.12 0.4146 9.67 0.000 2.10 0.062 R squared = 30.6% R squared (adjusted) = 23.7% s = 10.42 with 12-2 = 10 degrees of freedom Part A: Provide the regression equation based off the analysis provided & explain it in context. Part B: List the conditions for inference that need to be verified. Assuming these conditions have been met, does the data provide convincing evidence of a relationship between weight and top speed? Part C: Assuming all conditions for inference have been verified, determine a 95% confidence interval estimate for the slope of the regression line.

What information does a regression coecient provide that a correlation does not?

The accompanying Credit Approval Decisions data provide information on credit history for a sample of banking customers. Use regression analysis to identify the best model for predicting the credit score as a function of the other numerical variables, using both the p-value and t-statistic criteria. How do the models compare? Which would you choose? Use a level of significance of 0.05. E Click the icon to view the Credit Approval Decisions data. i........ Credit approval decisions data table ..... What is the best model using the p-value criteria? Select the best answer below and fill in the answer boxes to complete your choice. (Type integers or decimals rounded to three decimal places as needed.) Revolving Utilization (%) Credit Score Years of Credit Revolving Balance (S) History O A. Credit Score = ) Years + (O Balance 728 21 11,420 0.23 VB. Credit Score = 760.800 + (- 219.013) Utilization 576 8 7,300 0.71 680 13 20,100 0.52 O C. Credit Score = O Balance 628 17 12,900 0.68 530 10…

Use the scatterplot of Vehicle Registrations below to answer the questions Vehicle Registrations in the United States, 1925- 2011 Vehicles millions 300 y = 3.0161x - 5819.5 R² = 0.9695 250 200 150 100 50 1920 -50 1940 1960 1980 2000 2020 Year State the trend line (regression line). y= 3.0161 x -5819.5 year number of vehicle registrations R^2 = 0.9695 not enough information to determine Registrations (in millions)

The consumption function captures one of the key relationships in economics. It expresses consumption as a function of disposal income, where disposable income is income after taxes. The attached file "Regression-Dataset1" shows data of average US annual consumption (in $) and disposable income (in $) for the years 2000 to 2016. a)what is the Pearson correlation value between income and consumption? Select one: Ⓒa. 0.978 b. 0.109 c. None of the above d. 0.791

Let's study the relationship between brand, camera resolution, and internal storage capacity on the price of smartphones. Use α = .05 to perform a regression analysis of the Smartphones01CS dataset, and then answer the following questions. When you copy and paste output from MegaStat to answer a question, remember to choose to "Keep Formatting" to paste the text. a. Did you find any evidence of multicollinearity and variance inflation among the predictors. Explain your answer using a VIF analysis. b. Copy and paste the normal probability plot for your analysis. Is there any evidence that the errors are not normally distributed? Explain. c. Copy and paste the Residuals vs. Predicted Y-values. Does the pattern support the null hypothesis of constant variance for the errors? Explain. d. Study the residuals analysis. Which observations, if any, have unusual residuals? e. Study the residuals analysis. Calculate the leverage statistic. Which observations, if any, are high leverage…

Identify the type of data that would be used when the variable of interest is most-watched TV show.

Describe the correlation coefficient and explain how it is used in regression analysis?