Be sure to show you setups for all the problems
7. Predict the number of Cars through the gate if the price is $8 and the average December temperature is 30 degrees.
Dependent Variable |
CARS |
N |
15 |
Multiple R |
0.8298604 |
Squared Multiple R |
0.6886683 |
Adjusted Squared Multiple R |
0.6647197 |
Standard Error of Estimate |
478.7469853 |
Regression Coefficients B = (X'X)-1X'Y |
||||||
Effect |
Coefficient |
Standard Error |
Std. Coefficient |
Tolerance |
t |
p-value |
CONSTANT |
7,553.2334688 |
580.7547678 |
0.0000000 |
. |
13.0058914 |
0.0000000 |
PRICE |
-633.9414634 |
118.2181417 |
-0.8298604 |
1.0000000 |
-5.3624719 |
0.0001292 |
Dependent Variable |
CARS |
N |
15 |
Multiple R |
0.9203313 |
Squared Multiple R |
0.8470097 |
Adjusted Squared Multiple R |
0.8215113 |
Standard Error of Estimate |
349.3071128 |
Regression Coefficients B = (X'X)-1X'Y |
||||||
Effect |
Coefficient |
Standard Error |
Std. Coefficient |
Tolerance |
t |
p-value |
CONSTANT |
5,017.0284102 |
835.1434178 |
0.0000000 |
. |
6.0073854 |
0.0000615 |
PRICE |
-613.3296817 |
86.4533074 |
-0.8028786 |
0.9954233 |
-7.0943461 |
0.0000126 |
PITTDECT |
73.4854826 |
20.8519046 |
0.3988350 |
0.9954233 |
3.5241617 |
0.0041912 |
Case |
CARS |
PRICE |
PITTDECT |
1 |
6000 |
3 |
33.9 |
2 |
5966.7 |
3 |
31.7 |
3 |
4697.3 |
4 |
38.2 |
4 |
4436.8 |
4 |
27.7 |
5 |
4760 |
4 |
36.5 |
6 |
5072.8 |
4 |
33.5 |
7 |
5066 |
5 |
37.8 |
8 |
4446 |
5 |
34.6 |
9 |
3455.4 |
5 |
23.1 |
10 |
4781.4 |
5 |
37.5 |
11 |
3865.3 |
6 |
30.7 |
12 |
3447.8 |
6 |
32.6 |
13 |
3711 |
6 |
33.3 |
14 |
3448.2 |
6 |
27.6 |
15 |
4500.2 |
6 |
38.8 |
16 |
. |
. |
. |
Be sure to show you setups for all the problems Examine the computer output for Equation...
You are given different sets of Stata output below. Please use the appropriate Stata output to answer questions below.log price is the natural log of price. a. Write the estimated equation from a regression of log price on mpg, weight, headroom, and trunk. Interpret each coefficient. b. Test if headroom and trunk have no effect on price. Please show your work. Source | SS df MS Model Residual 3.82089653 7.40263655 4 69 .955224132 .107284588 Number of obs = FC 4,...
(13 points) Suppose you have a simple linear regression model such that Y; = Bo + B18: +€4 with and N(0,0%) Call: 1m (formula - y - x) Formula: F=MSR/MSE, R2 = SSR/SSTO ANOVA decomposition: SSTOSSE + SSR Residuals: Min 1Q Modian -2.16313 -0.64507 -0.06586 Max 30 0.62479 3.00517 Coefficients: Estimate Std. Error t value Pr(> It) (Intercept) 8.00967 0.36529 21.93 -0.62009 0.04245 -14.61 <2e-16 ... <2e-16 .. Signif. codes: ****' 0.001 '** 0.01 '* 0.05 0.1'' 1 Residual standard...
Problem 5- Simple Linear Regression The following data represent the number of flash drives sold per day at a local computer shop and their prices Price $34 36 32 35 30 Units Sold 6 40 A computer output is produced to examine this relationship further SUMMA RY OUTPUT Regression Statistics Multiple R RSquare Adjusted R Square Standard Error Observations 0.924982 0.855592 0.826711 1.119949 7 ANOVA MS gnificance F Regression Residual Total 137.15714 37.15714 29.62415 0.002842 5 б,271429 1.254286 6 43.42857...
show all steps, excel not allowed, thank you and will rate
Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.989778267 0.979661017 0.969491525 0.387298335 4 ANOVA Significance F 14.45 96.33333 0.010221733 MS Regression Residual Total 14.45 0.3 14.75 0.15 Coefficients p-value Lower 95% U per 95% Lower 95.096 Upper 95.0% 7 0.474341649 14.75729575 0.00456 4.959072609 9.040927 4.959072609 9.040927391 1.7 0.173205081-9.814954576 0.010222 -2.445241314 0.95476-2.445241314 -0.954758686 Standard Errort Stat Intercept Case Sales a. Write the regression equation for the...
"By Hand" Problems: For hypothesis tests, you may use R to find the p-value. For confidence intervals, you may use R to find the multiplier 1. (Continuation of HW 7, Problem 3) Suppose it is of interest to examine the relationship between the size of cruise chips and their passenger capacity. A data from 158 cruise ships was collected based on the following two variables: . Size of the ship (Tonnage) the gross tonnage in 1000s of tons Maximum passenger...
y2 36 49 1760 You are a personnel director and are interested in predicting the Number of Shares of Company Stock (V) using the Number of Years Employed with the Company (X). You randomly selected 8 employees and obtain the following summary data: EmplNum NumberShares (Y) YearsEmployed (x) - XY X² 1 350 2100 122500 2 415 7 2905 172225 3 22 8 64 48400 4 520 4680 81 270400 5 355 10 3550 126025 640 8320 169 409600 7...
Please show work so that I can see the process also Let’s say you scored a 111 on exam 1 and you scored an 125 on exam 2. You can predict your final exam score with the following prediction equation: Y’ = bX + c (round to nearest whole number). X is the total number of points you earned on the first two tests. Given: Mean = 120; standard deviation of y = 100. The correlation (r) between the total...
24. United Widget Manufacturing has a problem with defective widgets. Employees make hundreds of thousands of them each day, and many are defective. United has instituted training for the workers, and you would like to predict the number of defects per week based on the number of days of training an employee has received. You obtain the following data: ST Employee number 1015 2023 1153 4029 1117 0012 Days of training 4 5 6 4 3 2 Defects per week...
Hello I need help with questions 2 until 9 if possible. If you
can please show all work and answers clearly. Thanks for all the
help have this project that’s due tonight so I need help on
thanks.
27 27889.0526471 10.12 1.09 28 SUMMARY OUTPUT 29 30 Regression Statistics 1 Multiple F 0.986442 32 R Square 0.973068 33 Adjusted 0.967681 34 Standard I 32.55341 35 Observati 36 37 ANOVA 38 39 Regressio 40 Residual 41 Total 42 43 44 Intercept...
UESTION 7 Fuel efficiency in auto-mobiles can be influences by a number of characteristics. See the linear regression output below and answer the following questions Results of linear regression analysis are shown below: Call: lm (formula = mpg ~ ., data = auto-mpg) Residuals: Min 1Q Median 3Q Max -8.6927-2.3864 -0.0801 2.0291 14.3607 Coefficients: Estimate Std. Error t value Pr>Itl) (Intercept) -1.454e+01 4.764e+00 -3.051 0.00244* cyl disp hp gvw accel year -3.299e-01 3.321e-01 -0.993 0.32122 7.678e-03 7.358e-03 1.044 0.29733 -3.914e-04...