In linear regression, how can you minimise the error between predicted and actual observed values?

The estimated regression line: a. does not change the sum of squared residuals.b. maximizes the sum of squared residuals.c. minimizes the sum of squared residuals.d. sometimes maximizes and sometimes minimizes the sum of squared residuals.can you also explain the answer, please

A county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model, y = b1x + bowhere y = appraised value of the house (in $thousands) and x = number of rooms. Using data collected for a sample of n=74 houses in East Meadow, the following results were obtained: y = 74.80 + 17.80x Give a practical interpretation of the estimate of the slope of the least squares line. For each additional room in the house, we estimate the appraised value to increase 74,800ドル. For each additional dollar of appraised value, we estimate the number of rooms in the house to increase by 17.80 rooms. For a house with O rooms, we estimate the appraised value to be 74,800ドル. For each additional room in the house, we estimate the...

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). The following table shows a portion of the regression results. ANOVA Significance df SS MS F F gression Residual 3 5694717 1898239 50.88 4.99E-13 36 1343176 37310 Total 39 7037893 Standard Upper 95% Coefficients Error t Stat p-value_Lower 95% Intercept 300 84.0 3.57 0.0010 130.03 470.79 Bed 226 60.3 3.75 0.0006 103.45 348.17 Bath 89 55.9 1.59 0.1195 -24.24 202.77 Sqft 0.2 0.09 2.22 0.0276 0.024 0.39 What would be the rent for a 1000-square-foot apartment that has 2 bedrooms and 2 bathrooms? 840ドル 1,335ドル 1,130ドル 1,260ドル

in multiple regression analysis, a residual is the difference between the value of a dependent variable and its corresponding independents variable value? True or false?

How does linear regression differ from analysis of variance?

State whether the slope of a simple linear regression line is statistically significant, then the correlation will also always be significant?

A regression line for predicting test scores has an r.m.s. error of 8 points. When this result is used to predict test scores, about 95% of the predictions will be right to within how many points.

Using a sample of recent university graduates, you estimate a simple linear regression using initial annual salary as the dependent variable and the graduate's weighted average mark (WAM) as the explanatory variable. If the regression model has an estimated intercept of 3200 and an estimated slope coefficient of 550, what is the predicted starting salary of a student with a WAM of 64?

You are analyzing a dataset with 932 datapoints. You decide to create a linear regression model with this dataset, using 18 predictor variables. Using this information, what is the degrees of freedom associated with the sum of squares total (total sum of squares) of your analysis?

In multiple regression analysis, explain why the typical hypothesis that analysts want to test is whether a particular regression coefficient (B) is equal to zero (H0: B = 0) versus whether that coefficient is not equal to zero

For a two-variable linear regression, if the sample correlation between the depen- dent and independent variables is –0.7, then the independent variable explains 49% of the variation in the dependent variable. Is this statement true? Explain your answer.

how you will calculate the degrees of freedom in multiple linear regression and in simple linear regression?