Q4. The Omantel firm has estimate the Sales of fibre internet connections in Oman with the related to advertising expenditure made by the company over the past 26 months. Following is the firm estimated results of the regression equation. DEPENDENT VARIABLE: Y R-SQUARE F-RATIO P-VALUE ON F OBSERVATIONS: 26 0.85121212 8.747 0.0187 PARAMETER STANDARD VARIABLE ESTIMATE ERROR T-RATIO P-VALUE INTERCEPT 7.6 6.33232 1.200 0.2643969 3.53 0.52228 0.0001428 a. What is the dependent and independent variables in the above regression equation of Omantel firm? b. Calculate the estimated t-ratio. c. Test the slope estimates for statistical significance at the 10 percent significance level.
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- A sociologist was hired by a large city hospital to investigate the relationship between the number of unauthorized days that employees are absent per year and the distance (miles) between home and work for the employee. A sample of 10 employees was chosen, and the following data were collected. A. Is the estimated regression equation appropriate and adequateAccording to World Health Organization (WHO), the recommended limit for a noise level inside a classroom is 35 dBA. However, nine out of ten schools fail to meet this recommendation. A researcher wishes to conduct a study relevant to the prior information, but as a gap, he decides to include the area (in square meters) of every classroom and how it could possibly affect the resulting noise level. He selects 17 classrooms at random, and the noise levels are recorded in the next slide. a. Find the regression equation and construct the scatter plot diagram. b. Predict the noise level if a classroom has an area of 85.97 m2 . c. Calculate the coefficient of determination and interpret the findings. d. Calculate the coefficient of alienation and interpret the findings. Use ExcelThe systolic blood pressure dataset (in the third sheet of the spreadsheet linked above) contains the systolic blood pressure and age of 30 randomly selected patients in a medical facility. What is the equation for the least square regression line where the independent or predictor variable is age and the dependent or response variable is systolic blood pressure? Y=__________ X + ______________ Patient 7 is 67 years old and has a systolic blood pressure of 170 mm Hg. What is the residual? __________ mm Hg Is the actual value above, below, or on the line? What is the interpretation of the residual? (difference in actual &predicated bp, difference in age, the amount of systolic changes)
- The accompanying data resulted from an experiment in which weld diameter and shear strength (in pounds) were determined for five different spot welds on steel. Below are the data collected and the regression equation. Diameter Strength 200.1 813.7 210.1 785.3 220.1 960.4 230.1 1118.0 240.0 1076.2 Strength = -941.6992 + 8.5988*Diameter The predicted y-hat value for a diameter of 201 is 864. if we observed a weld that had a diameter of 235 that had a strength 1000, what would be its residual?A Ross MAP team is currently developing a regression model to explain the travel expense of HR consulting firms in a month (measured in thousands of dollars). So far, the team has identified the number of consultants, the number of clients, the number of air-travel trips, and the number of trips to high-expense cities (e.g., NYC, Boston, San Jose) as potential independent variables. A partial output of the corresponding regression model is in Figure 1. Use the figure to answer question 4to6 4. What is the R2 and adjusted R2 of the model? 5. What is the standard error of the estimates (serror) in thousands of dollars? 6. Based on what you can learn from this table, what is your assessment about the model? For your information, the firm with the lowest travel expense was $47K and the firm with the highest expense was $125K in the sample data.The linregress() method in scipy module is used to fit a simple linear regression model using “Reaction” (reaction time) as the response variable and “Drinks” as the predictor variable. The output is shown below. What is the correct regression equation based on this output? Is this model statistically significant at 5% level of significance (alpha = 0.05)? Select one. Python script outputs for the linregress method. Slope equals 6.0000, intercept = 3.9999, rvalue = 0.9728, pvalue = 0.0011, stderror = 0.7141. Note: Python output shows many decimal places. All values Question 2 options: Reaction = 6.0000 + 3.9999 Drinks, model is not statistically significant Reaction = 6.0000 + 3.9999 Drinks, model is statistically significant Reaction = 3.9999 + 6.0000 Drinks, model is not statistically significant Reaction = 3.9999 + 6.0000 Drinks, model is statistically significant
- The Wall Street Journal asked Concur Technologies, Inc., an expense management company, to examine data from 8.3 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 (X) and the average amount spent on entertainment (Y) for a random sample of 9 of the 25 most-visited U.S. cities. These data lead to the estimated regression equation y = 17.49 + 1.0334x. For these data SSE = 1541.4. Use Table 1 of Appendix B. a. Predict the amount spent on entertainment for a particular city that has a daily room rate of $89 (to 2 decimals). b. Develop a 95% confidence interval for the mean amount spent on entertainment for all cities that have a daily room rate of $89 (to 2 decimals). c. The average room rate in Chicago is $128. Develop a 95% prediction interval for the amount spent on entertainment in Chicago (to 2 decimals).The Wall Street Journal asked Concur Technologies, Inc., an expense management company, to examine data from 8.3 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 (X) and the average amount spent on entertainment (Y) for a random sample of 9 of the 25 most-visited U.S. cities. These data lead to the estimated regression equation y = 17.49 + 1.0334x. For these data SSE = 1541.4. Use Table 1 of Appendix B. (NEED ANSWER FOR A) a. Predict the amount spent on entertainment for a particular city that has a daily room rate of $89 (to 2 decimals).The owner of Original Italian Pizza restaurant chain wants to understand which variable most strongly influences the sales of his specialty deep-dish pizza. He has gathered data on the monthly sales of deep-dish pizzas at his restaurants and observations on other potentially relevant variables for each of several outlets in central Indiana. These data are provided in the file P10_04.xlsx. Estimate a simple linear regression equation between the quantity sold (Y) and each of the following candidates for the best explanatory variable: average price of deep-dish pizzas (X1), monthly advertising expenditures (X2), and disposable income per household in the areas surrounding the outlets (X3). Round your answers for intercept coefficients to the nearest whole number and slope coefficients to two decimal places, if necessary. If your answer is negative number, enter "minus" sign.
- Suppose the following data were collected from a sample of 15 houses relating selling price to square footage and the architectural style of the house. Use statistical software to find the following regression equation: PRICEi=b0+b1SQFTi+b2COLONIALi+b3RANCHi+ei . Is there enough evidence to support the claim that on average, houses that are ranch style have lower selling prices than houses that are Victorian style at the 0.05 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."Selling Price Square Footage Colonial (1 if house is Colonial style, 0 otherwise) Ranch (1 if house is Ranch style, 0 otherwise) Victorian (1 if house is Victorian style, 0 otherwise) 377640 1941 1 0 0 460996 3397 0 1 0 405781 2764 0 0 1 407216 2906 0 0 1 435139 3401 1 0 0 405275 2600 0 0 1 381141 2203 0 1 0 370490 2046 1 0 0 404070 2210 0 0 1 460196 3692 0 1 0 382780 2172 1 0 0 406466 2606 0 1…The sheet called HousePr contains data on prices of houses that have sold recently and two attributes of the house – the number of bedrooms and the size. Column 1 is the selling price of the house in thousands of dollars and column 2 is the size in hundreds of square feet.A. What is the expected price for a house with size 2000 square feet? Using relevant Excel output, discuss whether the margin of error of this expected price will be low or high.B. Using Excel, obtain the equation of the linear regression line that fits this data for price vs. number of bedrooms. Is the true slope different from zero?C. Which of these two variables – number of bedrooms or size, is the better predictor for price and why? Price SqrFoot Bedrooms731 21 4901 22 4736 16 3866 19 3697 12 2836 17 3694 17 3843 18 3721 15 4883 18 2913 17 3868 17 3642 17 3884 17 3810 16 2841 16 3779 17 4726 16 3667 14 2870 21 4834 16 2776 19 4681 14 2846 19 4917 20 4946 23 5813 20 4911 18 3723 17 3711 15 2897 19 4881 22 4863 19…In a manufacturing process the assembly line speed (feet per minute) was thought toaffect the number of defective parts found during the inspection process. To test thistheory, managers devised a situation in which the same batch of parts was inspectedvisually at a variety of line speeds. They collected the following data.Line SpeedNumber of DefectiveParts Found20 2120 1940 1530 1660 1440 17a. Develop the estimated regression equation that relates line speed to thenumber of defective parts found.b. At a .05 level of significance, determine whether line speed and number ofdefective parts found are related.c. Did the estimated regression equation provide a good fit to the data?d. Develop a 95% confidence interval to predict the mean number of defectiveparts for a line speed of 50 feet per minute.