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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