Imagine a regression with YEAR (as in 2015 or 2001) predicting SALES (in thousands of dollars) as so: SALES = 100 + 0.5*YEAR And YEAR is statistically significant. What should you predict SALES to be in 2020? (Express your answer as SALES does: in thousands of dollars.)

Imagine a regression with YEAR (as in 2015 or 2001) predicting SALES (in thousands of dollars) as so: SALES = 100 + 0.5*YEAR And YEAR is statistically significant. What should you predict SALES to be in 2020? (Express your answer as SALES does: in thousands of dollars.)

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

3. Describe the problem that outliers present for a regression analysis and outline what you could do to resolve this problem.
Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion). Use the horizontal axis for the customer age to graph. Find the linear regression line that models the data by each type of customer. Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales. What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y / Change in x
An e-commerce retailer was trying to understand how customer engagement correlates to customer purchases. They built the following regression model: Probability of Purchase = 0.1619 + 0.07 Emails Opened. If someone opens 5 emails how likely are they to purchase? If someone has a 75% chance of purchase how many emails have they opened? If someone opens no emails how likely are they to purchase? What is the slope here and what does it tell us?
Eloise needs to finish a 516-page report on international trade so she can present it at the big conference this weekend. She's been tracking her page count at the end of each business day to see if she's on track. Use Excel to fit a trendline to Eloise's data to create a model of her productivity. According to your model, will Eloise be able to finish her report by the end of the day next Friday? Assume she works only on weekdays. Day Pages Monday 425 Tuesday 431 Wednesday 449 Thursday 464 Friday 471 A: Use Excel to fit a trendline to Eloise's data. What are the slope and intercept for this line? The slope is The intercept is B: Use your model (trendline) to answer the question. Will Eloise finish her report by the end of next friday? Yes
Smith is studying the relationship between length (X) and weight (Y) of infants. Please help him fit an exponential growth model to find the relationship between length and weight. Write down the model and explain how to fit the model (i.e., how to estimate regression coefficients) and what the intercept is for the relationship between X and Y (in symbolic form).
The regression line, y = 23.4 -0.07x, passes the horizontal axis at 334.3 -0.07 O -23.4 0.07 23.4 O-334.3
Recently there has been a rise in fatal accidents caused by “distracted driving.” The United States National Highway Traffic Safety Administration (NHSTA) defines a distraction as anything that diverts attention from driving. The following model estimates the trend in distracted driving accidents over time based on data from the NHSTA:D = 0.015t - 29.97 In this model, D is the decimal to be turned into a percentage of all fatal accidents caused by distracted driving, and t is any calendar year between 2000 and 2020. Use the model to estimate the rate for 2005 and 2020. Note: this means to plug n 2005 for t and simplify and then turn your answer into a percent. Then plug in 2020 for t and simplify and then turn your answer into a percent.
Describe three approaches to modeling seasonality in a regression forecast.
The fitted regression is Sales = 842 − 37.5 Price. (a-1) If Price = 1, what is the prediction for Sales? (Round your answer to 1 decimal place.) (b) If Price = 20, what is the prediction for Sales?
This is a regression model of how many potato chips one can eat while watching a movie: Y=-2.014*x+40 and the R squared is 0.5 How many chipsp are to be eaten in a 12 hour period?
In running a logistic regression, a statistician decided to include decade of life (20s, 30s, 40s, 50s, 60s) as a categorical variable in the systematic component because they didn't believe the relationship between the log-odds and age was linear. Using dummy variables to create the systematic component, how many dummy variables would be needed to represented age?