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R Programming · all questions

Data Visualization with ggplot2
practice.

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If Linear regression model perfectly first i.e., train error is zero, then . . . . . . . .

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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

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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?

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Function used for linear regression in R is . . . . . . . .

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In syntax of linear model lm(formula,data,..), data refers to . . . . . . . .

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In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to . . . . . . . .

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. . . . . . . . is an incredibly powerful tool for analyzing data.

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The square of the correlation coefficient r 2 will always be positive and is called the . . . . . . . .

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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 . . . . . . . .

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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?

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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.

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Although it may seem overly simplistic, . . . . . . . . is extremely useful both conceptually and practically.

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When there are more than one independent variables in the model, then the linear model is termed as . . . . . . . .

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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 . . . . . . . .

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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?

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. . . . . . . . regression method is also known as the ordinary least squares estimation.

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. . . . . . . . refers to a group of techniques for fitting and studying the straight-line relationship between two variables.

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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 . . . . . . . .

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