To predict the price of a home in Ulster County, NY data on the following factors (number of bathrooms, the square footage of the house, and the age of the house (yrs) were collected. A regression analysis is given below.
| ANOVA | ||||
| df | SS | MS | F | |
| Regression | 3 | 899875 | 299958.4 | |
| Residual | 24 | 151882 | 6328.4 | |
| Total | 27 | 1051757 | ||
| P-value | ||||
| Intercept | 0.004 | |||
| Bathrooms | 0.995 | |||
| Sq. ft. | 0.000 | |||
| Age (yrs) | 0.040 |
Name the variables that are influential at the 0.05 level.
The following questions pertain to the overall significance of the variables on annual snowfall.
Compute the test statistic F (1 decimal place)
Name the critical value
Draw a conclusion regarding the overall significance of the variables on a annual snowfall
i)
P-values of the square footage of the house and the age of the house (yrs) are less than 0.05. then these two are influential at 0.05 level
ii)
F = MS_Regreession / MS_error = 299958.4/6328.4 = 47.39877
F critical at 0.05 level of significance and (df_Regression, df_error) = (3, 24) is equals to 3.008787
as the F > F_critical then we reject H0 that implies overall variables are significant
To predict the price of a home in Ulster County, NY data on the following factors...
A real estate agent wants to use a multiple regression model to predict the selling price of a home in thousands of dollars) using the following four x variables. Age: age of the home in years Bath: total number of bathrooms LotArea: total square footage of the lot on which the house is built TotRms_AbvGrd: total number of rooms (not counting bathrooms) in the house The agent runs the regression using Excel and gets the following output. Some of the...
Data from n = 113 hospitals in the United States are used to assess factors related to the likelihood that a hospital patients acquires an infection while hospitalized. The variables here are y = infection risk, x1 = average length of patient stay, x2 = average patient age, x3 = measure of how many x-rays are given in the hospital. The Minitab output is as follows: Regression Analysis: InfctRsk versus Stay, Age, Xray Analysis of Variance Source DF Adj SS...
What is test statistic?
What is p-value?
b. Construct a 95% confidence interval for each regression
coefficient and interpret its meaning.
utility company would like to predict the monthly heating bill for a household in a certain county in January. A random sample of households in the county was selected and their January heating bill was recorded along with the variables SF, Age, and Temp, with the results shown i i Click the icon to view the regression output. the...
A used car salesman wants to explain car price ($1,000s) using car age (years). A sample of midsized sedans was obtained. The output from a simple linear regression on the data is below. Parameter Estimate Std. Err. DF T-Stat P-value Intercept 17.370 1.448 8 11.31 0.000 Slope - 1.2283 0.2130 8 -5.77 0.001 Analysis of variance table for regression model: Source DF SS MS F-stat P-value Model 1 138.79 138.79 33.26 0.001 Error 8 29.21 4.17 Total 9 168.00 S...
4a A real estate analyst believes that the three main factors that influence an apartment’s rent in a college town are the number of bedrooms, the number of bathrooms, and the apartment’s square footage. For 40 apartments, she collects data on the rent (y, in $), the number of bedrooms (x1), the number of bathrooms (x2), and its square footage (x3). She estimates the following model as Rent = β0 + β1Bedroom + β2Bath + β3Sqft + ε. The following...
5. 1 Data were collected for a random sample of 220 home sales from a U.S. community in 2003 Let Price denote the selling price (in $1000), BDR the number of bedrooms, Bath the number of bathrooms, Hsize the size of the house (in sq. ft.), Lsize the lot size (in sq. ft.), Age the age of the house (in years), and Poor a binary variable that is equal to 1 if the condition of the house is reported as...
a. $48,626
b. $97,252
c. $28,545
d. none of the above
From the regression example discussed in class and based on the information below, what is the impact on the price of a house if you add two bathrooms? SUMMARY OUTPUT Regression Statistics Multiple R 0.92 R Square 0.85 Aqusted R Square 0.84 Standard Error 32685.63 Observations ANOVA MS F 43.60 O 46,578,674,391.09 1,068,350,694.88 Significance F 0.00 Regression Residual Total SS 5 232,893,371,965.43 37 39,528,975,710.43 42 272.422,347 665.86 Stat Intercept...
18
QueSLIVIT TO Based on the following regression output, what is the equation of the regression line? Regression Statistics Multiple R 0.99313 0.98630 R Square Adjusted R Square Standard Error 0.98238 2.94802 10 Observations ANOVA df SS MS Significance F Regression 4379.182 2189.591 251.943 0.0000 Residual 7 60.836 8.691 9 Total 4440.017 Coefficients Standard Error t Stat P-value Lower 95% 14.169 3.856 3.674 Intercept 0.008 5.050 X Variable 1 0.985 0.114 8.607 0.000 0.714 X Variable 0.995 0.057 17.498 0.000...
QUESTION 27 Q27. A manager at a local bank analyzed the relationship between monthly salary (y, in $) and length of service (x, measured in months) for 30 employees. She estimates the model: Salary = Bo + B1 Service + ε. The following ANOVA table below shows a portion of the regression results. df SS M S F Regression 555,420 555,420 7.64 Residual 27 1,962,873 72,699 Total 28 2 ,518,293 Coefficients Standard Error t-stat p-value Intercept 784.92 322.25 2.44 0.02...
Data were collected from a random sample of 220 home sales from a community. Let Price denote the selling price (in $1000), BDR denote the number of bedrooms, Bath denote the number of bathrooms, Hsize denote the size of the house (in square feet), Lsize denote the lot size (in square feet), Age denote the age of the house (in years), and Poor denote a binary variable that is equal to 1 if the condition of the house is reported...