XYZ Training wants to know if they can use data from the past 12 months of numbe of monthly clients (x) and profit for each corresponding month (y) to perform a regression analysis. Examine their Excel regression printout. Identify the y intercept of the regression line. Round answers to 3 decimal places. SUMMARY OUTPUT Regression Sta tistics Multiple R 0.891759168 R Square 0.795234414 Adjusted R Square 0.774757855 Standard Error 1841.207101 Observations 12 ANOVA df SS MS F Regression 1 131656855.8 1.32E+08 38.836331 Residual 10 33900435.9 3390044 Total 11 165557291.7 Coefficients Standard Error t Stat P-value 1489.764874 2.219215 Intercept XVariable 1 3306.109257 0.050762 186.8522243 29.9832823 6.23188 9.732E-05 -3306.109 3306.109 186.852 -186.852

XYZ Training wants to know if they can use data from the past 12 months of numbe of monthly clients (x) and profit for each corresponding month (y) to perform a regression analysis. Examine their Excel regression printout. Identify the y intercept of the regression line. Round answers to 3 decimal places. SUMMARY OUTPUT Regression Sta tistics Multiple R 0.891759168 R Square 0.795234414 Adjusted R Square 0.774757855 Standard Error 1841.207101 Observations 12 ANOVA df SS MS F Regression 1 131656855.8 1.32E+08 38.836331 Residual 10 33900435.9 3390044 Total 11 165557291.7 Coefficients Standard Error t Stat P-value 1489.764874 2.219215 Intercept XVariable 1 3306.109257 0.050762 186.8522243 29.9832823 6.23188 9.732E-05 -3306.109 3306.109 186.852 -186.852

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

• When fitting a linear regression, multiple regression lines may be equally as good to represent the data. True O False
Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child (height ), height of the mother ( mothersheight), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS df 208.008457 314.295372 37 2 104.004228 8.49446952 MS 522.303829 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared Root MSE = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .9567683 .4804261 34.92886 What is the predicted height for a child born to a mother…
The money raised and spent (both in millions of dollars) by all congressional campaigns for 8 recent 2-year periods are shown in the table. The equation of the regression line is y = 0.940x + 34. 128. Find the coefficient of determination and interpret the result. 785.8 784.5 1032.7 963.8 1211.5 Money raised, x Money spent, y 456.6 654.6 733.8 449.4 691.4 734.6 764.4 739.3 1018.6 930.3 1172.9 Find the coefficient of determination and interpret the result. 2 = 0.989 (Round to hree decimal places as needed.)
How does R treat those observations with missing values? In other words, what role do the observations with missing values play in the regression? No command is needed for this question. You must need to provide an answer or take a guess.
The data provided give the number of standby hours based on total staff present, X₁, and remote hours, X₂. Perform a multiple regression analysis using the data provided and determine the VIF for each independent variable in the model. Is there reason to suspect the existence of collinearity? Click the icon to view the data. Determine the VIF for each independent variable in the model. | VIF ₂2 = VIF₁ = (Round to three decimal places as needed.) Is there reason to suspect the existence of collinearity? OA. No. The VIF for each independent variable is less than 5. B. Yes. The VIF for each independent variable is greater than 5. OC. No. The VIF for each independent variable is greater than 5. OD. Yes. The VIF for each independent variable is less than 5. wwwwwww Table of Data Standby Total Staff Hours Present 245 330 274 358 195 197 117 153 115 275 200 236 336 339 321 303 286 329 352 323 Remote Hours 417 655 524 385 349 350 388 153 278 479 Print Done
Briefly describe what is meant by the problem of errors in measurement of the predictor variables and describe its effect on a regression analysis.
Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion). Use the horizontal axis for the customer age to graph. Find the linear regression line that models the data by each type of customer. Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales. What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y / Change in x
Please answer letters b-e.
i submitted a picture of the question
Describe regression variation in terms of variation in Y.
Using the data in the Excel spreadsheet "auto.xlsx", estimate a regression to answer the following question. A 1 inch increase in LENGTH corresponds to a _________________ increase in MPG (miles per gallon). Use negative values if it results in a decreased price. Round your answers to two decimals. make price mpg rep78 headroom trunk weight length turn displacement gear_ratio foreign AMC Concord 4,099 22 3 3 11 2,930 186 40 121 3.58 Domestic AMC Pacer 4,749 17 3 3 11 3,350 173 40 258 2.53 Domestic AMC Spirit 3,799 22 3 12 2,640 168 35 121 3.08 Domestic Buick Century 4,816 20 3 5 16 3,250 196 40 196 2.93 Domestic Buick Electra 7,827 15 4 4 20 4,080 222 43 350 2.41 Domestic Buick LeSabre 5,788 18 3 4 21 3,670 218 43 231 2.73 Domestic Buick Opel 4,453 26 3 10 2,230 170 34 304 2.87 Domestic Buick Regal 5,189 20 3 2 16 3,280 200 42 196 2.93 Domestic Buick Riviera 10,372 16 3 4 17 3,880 207 43 231 2.93 Domestic Buick Skylark 4,082 19 3 4 13 3,400 200 42 231…
Compute the least-squares regression equation for the given data set. Round the slope and yFintercept to at least four decimal places. 4 y 7. 3. Send data to Excel Regression line equation: y =|