In the macro relation between GDP and FDI, Dr Mudenda obtained the following values from a Stata output: FDI coefficient of 0.45; t-statistic of 3; and covariance (GDP, FDI) value of 0.41. In literature, this relationship between GDP and FDI has generally been established to be statistically significant. Answer the following questions: i) ii) Calculate the variance of the FDI variable and explain its implication Use the confidence interval approach to test whether FDI's influence on GDP is
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- Consider a linear regression model that relates school expenditures and family background to student performance in Massachusetts using 224 school districts. The response variable is the mean score on the MCAS (Massachusetts Comprehensive Assessment System) exam given in May 1998 to 10th-graders. Four explanatory variables are used: (1) STR is the student-to-teacher ratio, (2) TSAL is the average teacher’s salary, (3) INC is the median household income, and (4) SGL is the percentage of single family households. The Excel Regression output for the sample regression equation is given below. (a) What proportion of the variation in MCAS score is explained by the explanatory variables? (b) At the 5% level, are the explanatory variables jointly significant in explaining MCAS score? Explain briefly. (c) At the 5% level, which variables are individually significant at predicting MCAS score? Explain briefly. (d) Suppose a second regression model (Model 2) was generated using only…The accounting department analyzes the variance of the weekly unit costs reported by two production departments. A sample of 16 cost reports for each of the two departments shows cost variances of 2.6 and 5.3, respectively. Is this sample sufficient to conclude that the two production departments differ in terms of unit cost variance? You may need to use the appropriate technology to answer this question. Use a = 0.10. State the null and alternative hypotheses. Ho: 0, + 0,2 2 02 2 %D 2 Hoi 01 2 02 2 Ha: 01 2 > 02 Ho: 01 2 > 02 2 2 Ha: 02 2 2 Ho: 01 02 Ha: 01 + 02 Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to four decimal places. Remember: When using WebAssign technology to find p-values, use the entire test statistic (not the rounded answer from the previous question.)) p-value %3D State your conclusion. Reject Ho. We can conclude that there is a difference between the population variances. Reject Ho. We cannot…In order to be able to statistically determine which levels of the significant main effect(s) differ dec the analysis of variance, which of the following is performed? A)t-test B)Simple linear regression analysis C)Post-hoc analysis D)Analysis of residues
- A company wishes to estimate a regression line for the relationship between sales and advertising. а. To arrive at its decision regarding the best model to use, the company has calculated the following three correlation coefficients. i. ii. The sales in any month depend on that month's advertising with r = 0.28 The sales in any month depend on 50% of the previous month's advertising and 50% of that month's advertising with r= 0.68 The sales in any month depend on the previous month's advertising with r = 0.92 iii. Interpret each of the above correlation coefficients and state which of the suggested models you would choose as the basis for predicting sales. Justify your answer.The table below lists weights (carats) and prices (dollars) of randomly selected diamonds. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence o support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with a diamond that weighs 0.8 carats. Weight Price 0.3 $508 a. Find the explained variation. 0.4 $1153 0.5 $1332 Round to the nearest whole number as needed.) . Find the unexplained variation. Round to the nearest whole number as needed.) c. Find the indicated prediction interval.In a research setting, the efficiency of homecare workers (part-time and full-time) were compared. Data is given below. Is there a difference between full-time and part-time homecare workers? (Variances are assumed to be homogenous, α=0.05). Group Efficiency Points Group Efficiency Points Full-time 5 Part-time 1 Full-time 3 Part-time 3 Full-time 4 Part-time 4 Full-time 1 Part-time 4 Full-time 2 Part-time 2 Full-time 3 Part-time 0 Full-time 2The table below lists measured amounts of redshift and the distances (billions of light-years) to randomly selected astronomical objects. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 90% confidence level with a redshift of 0.0126. Redshift Distance 0.0238 0.31 a. Find the explained variation. 0.0543 0.74 (Round to six decimal places as needed.) b. Find the unexplained variation. (Round to six decimal places as needed.) c. Find the indicated prediction interval. 0.0722 1.02 billion light-yearsConsider a regression analysis with n= 51 and four potential independent variables. Suppose that one of the independent variables has a correlation of 0.49 with the dependent variable. Does this imply that this independent variable will have a very small Student's t statistic in the regression analysis with all four predictor variables? Choose all that apply. A. The correlation between the independent variable and the dependent variable could result in a very small Student's t statistic as the correlation creates a high variance. B. Correlation between the independent variable and the dependent variable is not necessarily evidence of a small Student's t statistic. C. A high correlation among the independent variables could result in a very small Student's t statistic as the correlation increases the coefficient standard errors. D. Correlation between the independent variable and the dependent variable is evidence of a small Student's t statistic.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…The following data represent the speed at which a ball was hit (in miles per hour) and the distance it traveled (in feet) for a random sample of home runs in a Major League baseball game in 2018. Complete parts (a) through (f). Click here to view the data. Click here to view the critical values of the correlation coefficient. (a) Find the least-squares regression line treating speed at which the ball was hit as the explanatory variable and distance the ball traveled as the response variable. y = 5x+ (5 (Round to three decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. Begin by interpreting the slope. A. The slope of this least-squares regression line says that the distance the ball travels increases by the slope with every 1 mile per hour increase in the speed that the ball was hit. O B. The slope of this least-squares regression line shows the increase in the speed that the ball was hit with every 1 foot increase in the distance that the ball was hit.…Consider the following information about variable X: Mean = 4, standard deviation = 2. Consider the following information about variable Y: Mean = 5, standard deviation = 3. If there is a correlation of r = 0.52 between variable X and variable Y, what is the intercept for a simple linear regression equation predicting Y on the basis of X? Please provide your answer as a raw score (not a z score) with a minimum of two decimal places.Recommended textbooks for youMATLAB: An Introduction with ApplicationsStatisticsISBN:9781119256830Author:Amos GilatPublisher:John Wiley & Sons IncProbability and Statistics for Engineering and th…StatisticsISBN:9781305251809Author:Jay L. DevorePublisher:Cengage LearningStatistics for The Behavioral Sciences (MindTap C…StatisticsISBN:9781305504912Author:Frederick J Gravetter, Larry B. 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