Starbucks brand now has much impact on many products including Yogurt. In this project, you are asked to collect the real estate data and model your data using regression techniques to study if there is a Starbucks effect in a given area of interest. Basically if there is an effect, it is expected that real estate is more competitive in the market and appear to be sold for higher price. The purpose of this project is to examine the relationship between the mean list price of a condo and the following property features: a) Floor square feet (numerical, square feet, take the midpoint if data is given as a range) b) Number of bedrooms (discrete numerical) c) Number of washrooms (numerical) d) Close to Subway or not (categorical, coded as Y or N) e) Maintenance fee (Monthly, numerical) f) Distance to nearest Starbucks is within 0.5 km or not (categorical, coded as Y or N
The built model has an equation which indicated the dependence of price on the vicinity of starbucks store. The model is coded in R and the script is attached.
John is planning to go on a cross country drive with his family. His Japanese minivan has been generally reliable but has 90,000 miles on it. So he wants to determine if the vehicle is mechanically sound for the arduous trip. He did a visual inspection of the vehicle and checked the oil and antifreeze levels and everything appears to look okay. The null and alternative hypotheses are given below. H0: minivan is mechanically sound Ha: minivan is not mechanically sound (a) What would a Type-I error be in this situation? (b) What would a type-II error be in this situation? (c) Which error is more consequential in this situation and why? (d) What impact would going to the mechanic have on the Type-I and Type-II errors and why?
a. Type 1 error would be to reject null hypothesis and in reality, it is true. In this case, John does not accept minivan is mechanically sound but factually the minivan is mechanically sound b. John accepts minivan to be mechanically true but in fact it is not c. In this case, making a type II error is more consequential. Going on cross country drive with family on a minivan is not going to be a good experience. d. Going to mechanic will impact for good in case of Type II error. In case of Type I error, after inspection the mechanic will call out for false negative.
Predict the number of bid receive for the online auction at ebay. The dataset contains all the necessary features.
Using the regression modelling, the most important features are: 1. Initial price 2. Time the bid is open 3. Product category The model built has an equation y=c + b1x1 + b2x2 + b3x3. Thus, we can predict the number of bid received given the required values.