If Linear regression model perfectly first i.e., train error is zero, then . . . . . . . .
A. Test error is also always zero
B. Test error is non zero
C. Couldn't comment on Test error
D. Test error is equal to Train error
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Which of the following metrics can be used for evaluating regression models?
i) R Squared
ii) Adjusted R Squared
iii) F Statistics
iv) RMSE / MSE / MAE
A. ii and iv
B. i and ii
C. ii, iii and iv
D. i, ii, iii and iv
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How many coefficients do you need to estimate in a simple linear regression model (One independent variable)?
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In a simple linear regression model (One independent variable), If we change the input variable by 1 unit. How much output variable will change?
A. by 1
B. no change
C. by intercept
D. by its slope
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Function used for linear regression in R is . . . . . . . .
A. lm(formula, data)
B. lr(formula, data)
C. lrm(formula, data)
D. regression.linear(formula, data)
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In syntax of linear model lm(formula,data,..), data refers to . . . . . . . .
A. Matrix
B. Vector
C. Array
D. List
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In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to . . . . . . . .
A. (X-intercept, Slope)
B. (Slope, X-Intercept)
C. (Y-Intercept, Slope)
D. (slope, Y-Intercept)
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. . . . . . . . is an incredibly powerful tool for analyzing data.
A. Linear regression
B. Logistic regression
C. Gradient Descent
D. Greedy algorithms
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The square of the correlation coefficient r 2 will always be positive and is called the . . . . . . . .
A. Regression
B. Coefficient of determination
C. KNN
D. Algorithm
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Predicting y for a value of x that's outside the range of values we actually saw for x in the original data is called . . . . . . . .
A. Regression
B. Extrapolation
C. Intra polation
D. Polation
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What is predicting y for a value of x that is within the interval of points that we saw in the original data called?
A. Regression
B. Extrapolation
C. Intra polation
D. Polation
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Analysis of variance in short form is?
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. . . . . . . . is a simple approach to supervised learning. It assumes that the dependence of Y on X1, X2, . . . Xp is linear.
A. Linear regression
B. Logistic regression
C. Gradient Descent
D. Greedy algorithms
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Although it may seem overly simplistic, . . . . . . . . is extremely useful both conceptually and practically.
A. Linear regression
B. Logistic regression
C. Gradient Descent
D. Greedy algorithms
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When there are more than one independent variables in the model, then the linear model is termed as . . . . . . . .
A. Unimodal
B. Multiple model
C. Multiple Linear model
D. Multiple Logistic model
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The parameter β0 is termed as intercept term and the parameter β1 is termed as slope parameter. These parameters are usually called as . . . . . . . .
A. Regressionists
B. Coefficients
C. Regressive
D. Regression coefficients
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The sum of squares of the difference between the observations and the line in the horizontal direction in the scatter diagram can be minimized to obtain the estimates is generally called?
A. reverse regression method
B. formal regression
C. logistic regression
D. simple regression
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. . . . . . . . regression method is also known as the ordinary least squares estimation.
A. Simple
B. Direct
C. Indirect
D. Mutual
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. . . . . . . . refers to a group of techniques for fitting and studying the straight-line relationship between two variables.
A. Linear regression
B. Logistic regression
C. Gradient Descent
D. Greedy algorithms
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In order to calculate confidence intervals and hypothesis tests, it is assumed that the errors are independent and normally distributed with mean zero and . . . . . . . .
A. Mean
B. Variance
C. SD
D. KNN
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