Hint: remember that a linear regression model is always linear in the parameters, but may use non-linear features. Question 1: Which of the following is NOT a linear regression model. Model 2 (Bedrooms) Week 2: Machine Learning: Regression Quiz Answer Quiz 1: Multiple Regression.Question 4: Which of the two models (square feet or bedrooms) has lower RSS on TEST data? Question 3: According to the inverse regression function and the regression slope and intercept from predicting prices from square-feet, what is the estimated square-feet for a house costing $800,000? You do not need to round your answer. Question 2: Using the learned slope and intercept from the squarefeet model, what is the RSS for the simple linear regression using squarefeet to predict prices on TRAINING data? 300000.34), and round your answer to 2 decimal places. Question 1: Using your Slope and Intercept from predicting prices from square feet, what is the predicted price for a house with 2650 sqft? Use American-style decimals without comma separators (e.g. d Quiz 2: Fitting a simple linear regression model on housing data.Which bold/labeled point, if removed, will have the largest effect on the fitted regression line (dashed)? Question 7: Consider the following data set, and the regression line fitted on this data: To make predictions for inputs in square meters, what slope must you use? Hint: there are 0.092903 square meters in 1 square foot. You believe that your housing market behaves very similarly, but houses are measured in square meters. The estimated intercept is -44850 and the estimated slope is 280.76. gives you a simple regression fit for predicting house prices from square feet. (Note: the next quiz question will ask for the slope of the new model.) To make predictions for inputs in square meters, what intercept must you use? Hint: there are 0.092903 square meters in 1 square foot.
If you extrapolate this trend forward in time, at which time index (in months) do you predict that your neighborhood’s value will have doubled relative to the value at month (index) 10? (Assume months are 0-indexed, round to the nearest month). Based on 10 months of data, the estimated intercept is $4569 and the estimated slope is 143 ($/month). You want to predict the average value of houses in your neighborhood over time, so you fit a simple regression model with average house price as the output and the time index (in months) as the input.
Question 4: You have a data set consisting of the sales prices of houses in your neighborhood, with each sale time-stamped by the month and year in which the house sold. This necessarily implies that these two people fit their models on exactly the same data set. You discover that the estimated intercept and slopes are exactly the same. Question 3: Two people present you with fits of their simple regression model for predicting house prices from square feet. Question 2: In a simple regression model, if you increase the input value by 1 then you expect the output to change by: In which interval would we expect predictions to do best? Question 1: Assume you fit a regression model to predict house prices from square feet based on a training data set consisting of houses with square feet in the range of 10. Get All Weeks Machine Learning: Regression Coursera Quiz Answers Week 1: Machine Learning: Regression Quiz Answers Quiz 1: Simple Linear Regression
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