An independent researcher wants to study the factors which affected the prices set by a leading AC manufacturing company, Chester, across the country for the year 2017. She studies whether the effect on price, of the quantity demanded of ACs by the consumers, is significant or not. She selects a random sample of 200 retail outlets of the company and estimates the following regression equation: P=800.75-12.58/n(Q), (8.98) (7.64) where P is the predicted value of price of the ACs and In(Q) is the logarithim of the quantity demanded of ACs by the consumers. Standard errors are given in parentheses. The 95% confidence interval for the slope coefficient, will be D (Round your answer to two decimal places. Enter a minus sign if your answer is negative.) Based on the calculated confidence interval, we can say that at the 5% significance level, we will the hypothesis that /,-0. fail to reject reject

The Conde Nast Traveler Gold List  for 2012 provided rating for the top 20 small cruise ships. The data from annual Readers’ Choice Survey are the overall scores(Y) each ship received based on several criteria, including Itineraries/Schedule (X1), Shore Excursions(X2), and   Food/Dinning(X3). The estimated regression equation to predict the overall scores is Y= 35.6184+0.1105 X1+0.2445 X2+0.2474 X3. Part of the regression results is shown below.                                                            Coefficients       Standard Error Intercept                                           35.6184            13.2308 Itineraries/Schedule(X1)                0.1105               0.1297 Shore Excursions(X2)                      0.2445               0.0434 Food/Dinning(X3)                            0.2474               0.0621     Use the T test to determine whether or not the coefficient of X1 is significant. Use Level of significance=.05? Be sure to state null and alternative hypotheses.…

In a regression study, relating Price/unit (x) to Weekly Sales (in Kg.), with the scatter plot showing a strong negative direction, 63% of the variability in sales could be accounted for by the variation in the Unit Price. The correlation coefficient in this study is: 0.7 -0.4 -0.79 0.4

The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the number of bids an item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 124 143 158 160 196 Number of Bids 12 13 15 16 20 Table   Step 2 of 6 :  Find the estimated y-intercept. Round your answer to three decimal places.

You are analyzing a dataset containing 379 datapoints, and want to use 13 predictor variables to create a multiple variable linear regression model of the data. You conduct an ANOVA analysis, and yield a R² of 38%. Using this information, what would be the F statistic of your analysis?

In a regression study, relating Price/unit (x) to Weekly Sales (in Kg.), with the scatter plot showing a strong negative direction, 63% of the variability in sales could be accounted for by the variation in the Unit Price. The correlation coefficient in this study is: 0.79 -0.4 -0.79 0.4

Suppose the following data were collected from a sample of 1515 CEOs relating annual salary to years of experience and the economic sector their company belongs to. Use statistical software to find the following regression equation: SALARYi=b0+b1EXPERIENCEi+b2SERVICEi+b3INDUSTRIALi+eiSALARY�=�0+�1EXPERIENCE�+�2SERVICE�+�3INDUSTRIAL�+��. Is there enough evidence to support the claim that on average, CEOs in the service sector have lower salaries than CEOs in the financial sector at the 0.010.01 level of significance? If yes, write the regression equation in the spaces provided with answers rounded to two decimal places. Else, select "There is not enough evidence." Copy Data CEO Salaries Salary Experience Service (1 if service sector, 0 otherwise) Industrial (1 if industrial sector, 0 otherwise) Financial (1 if financial sector, 0 otherwise) 144225144225 1010 11 00 00 187765187765 2020 00 00 11 142500142500 66 11 00 00 169650169650 2828 11 00 00 167250167250 3131 00…

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.   a. Predict the sales next month for an employee with 2.5 years of experience.   The predicted sales is 8.6 cars.   b. Compute the coefficient of determination and interpret its meaning.   The coefficient of determination is 0.234 ​ ​Therefore, about _________% of the variation in monthly sales is explained by the years of sales experience.   ​(Type an integer or decimal rounded to one decimal place as​ needed.)

The value of PRESS for five different regression models are A = 42.3, B = 47.4, C = 51.5, D =40.6, and E = 54.6. Based only on this statistic, which model is preferred?

The Wall Street Journal asked Concur Technologies, Inc., an expense management company, to examine data from  million expense reports to provide insights regarding business travel expenses. Their analysis of the data showed that New York was the most expensive city. The following table shows the average daily hotel room rate () and the average amount spent on entertainment () for a random sample of  of the  most-visited U.S. cities. These data lead to the estimated regression equation . For these data . Click on the datafile logo to reference the data. Use Table 1 of Appendix B. full question attached in ss thanks for help aprpeicated aigjrowoirj