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1.Based on the hands on card “ OLS in Python Statsmodels” What is the value of the estimated coef for variable RM ?
Ans : 9.1021
2.Based on the hands on card “ OLS in Python Statsmodels”What is the value of the constant term ?
Ans : -34.6706
3.Based on the hands on card “ OLS in Python Statsmodels” What is the adjusted R sq value ?
Ans : 0.483
4.Based on the hands on card “ OLS in Python Statsmodels” What is the value of R sq ?
Ans : 0.484
5.Based on the hands on card “ OLS in Python Statsmodels” How many observations are there in the dataset ?
Ans : 506
6.Based on the hands on card “ OLS in Python Statsmodels” What is the value of R sq (uncentered)?
Ans : 0.901
7.Based on the hands on card “ OLS in Python Statsmodels” What is the value of the coef for variable RM ?
Ans : 3.6534
8.Based on the hands-on card “ OLS in Python Statsmodels” What is the value of R sq (uncentered)?
Ans : 0.901
9.Based on the hands-on card “ OLS in Python Statsmodels” What is the value of the standard error for the value RM?
Ans : 0.419
10.Based on the hands on card “MLR Hands On” Perform a correlation among all the independent variables . What is the correlation between variables NOX and DIS ?
Ans : -0.76923
11.Based on the hands on card “MLR Hands On” What is the P>|t| value for the ‘INDUS’ variable ?
Ans : 0.731
12.Based on the hands on card “MLR Hands On” What is the standard error for the constant term ?
Ans : 5.104
13.Based on the hands on card “MLR Hands On” What is the value of the estimated coef for the constant term ?
Ans : 36.4911
14.Based on the hands on card “MLR Hands On” what is the value of R sq ?
Ans : 0.741
15.Regression can show causal relationship between two variables.
Ans – False
16.In Multi Variable regression you predict one variable using more than one variable
Ans – True
17.The SSE depends on the number of observations in the data set
Ans – True
18.__ means predicting one variable from another.
Ans : Regress
19.What is the process of removing the mean and dividing the value by the standard deviation
Ans : Standatdization
20.__ is a unit less quantity
Ans : R Square
21.When two or more variables are correlated in a Multiple Regression Model , it is called as _______
Ans : Multi Collinearity
22.What is the process of rescaling the values in the range [0,1]
Ans : Normalization
23.What is the formula for root means square error ?
Ans – sqrt(SSE/n)
24.It is advised to omit a term that is highly correlated with another while fitting a Multiple Regression Model
Ans – True
25.When more variables are added in Multi Variable Regression the marginal improvement decreases as each variable is added. This term is called ?
Ans – Law of Diminishing Returns
26.R Square Value can be greater than zero
Ans : False
27.Arithmetic Mean can be used as a prediction measure.
Ans : True
28.What is the sum of standard error for the baseline model ?
Ans : SST
29.SSE is _ for the Line of Best Fit and _ for the baseline model
Ans : Small , Big
30.It is advised to go for a simpler model while fitting multiple regression for a dataset
Ans : True
31.What is the term that represents the difference between actual and predicted value called ?
Ans : Residual
32.What is the basic property of the model of best fit ?
Ans : Minimize Error
33.By adding multiple variables in Multi Variable Regression , the model accuracy ______
Ans : Increases
34.Sum of Squared error is a measure of standard for a Regression Line
Ans : True
35.What is the good range of correlation values to include in the regression model
Ans : -0.7 to + 0.7
36.What is the quantity that measures the strength of relationship between two variables ?
Ans : Correlation
37.pr(>|t|) term signifies how likely the estimated value is zero
Ans : True
38.It is OK to discard theoretical considerations for Statistical Measures
Ans : False