estimate this model: purchases = β0 + β1social + β2aggregator + β3substack The table provides data related to online purchases, focusing on variables such as the source, type of purchase, rating, payment method, device type, and media channels used. Below is a detailed transcription and explanation:### Table Columns:1. **Source**: Indicates the origin of the purchase, either 'home' or 'landing'.2. **Purchase**: Numeric value representing the number of purchases.3. **Rating**: Customer rating values on a scale from 0 to 5.4. **Payment**: Payment method utilized, including options such as 'credit' or 'other'.5. **Device**: Type of device used, categorized as 'mobile' or 'other'.6. **Media**: Numeric variable presumably indicating engagement level or type, ranging from 1 to 4.7. **Social**: Indicates whether social media was used, binary values (0 or 1).8. **Traditional**: Indicates use of traditional media, binary values (0 or 1).9. **Aggregator**: Shows whether an aggregator platform was used, binary values (0 or 1).10. **Substack**: Indicates if Substack was used, binary values (0 or 1).### Data Sample:- **Row 1**: A purchase from 'home' with a total of 197, rating 1, using 'other' payment on 'mobile'. Media level/type 2, with no social or traditional media engagement, but aggregator was used, not Substack. - **Row 3**: A purchase from 'landing' with a total of 892, rating 4, using 'other' payment on 'mobile'. Media level/type is 4, and there's social media engagement but no traditional, aggregator, or Substack usage.### Interpretation:The data provides insights into consumer behavior based on the purchase source. It allows for the analysis of payment method preferences, device usage trends, and media channel effectiveness. This can guide marketing strategies and platform optimizations.This table aims to support educational purposes by demonstrating the application of data analytics in understanding consumer behavior in digital commerce.### No Graphs or Diagrams Included:There are no visual graphs or diagrams in the provided data set.

Given the regression function purchase depends upon media, aggregator and substack. We are required…

Answered: estimate this model: purchases = β0 +…

Business

Economics

estimate this model: purchases = β0 + β1social + β2aggregator + β3substack

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

See similar textbooks

Similar questions

look at the influence of mileage on the sale price of each brand ofautomobile. Postulate the following statistical models,?_ℎ????? = ?0 + ?1?????? + ?? and ?_?ℎ??? = ?0 + ?1?????? + ?? with ??~?(0, ?2).For each model (‘model_honda’ and ‘model_chev’) perform the following,Comment on the miles variable after examining it with hist() and summary() commands.What does the assumption of non-random independent variable imply about how thedata must be collected if the same miles variable is used for both brand of automobile?
Please no written by hand and no emage Your company, which specializes in running shoes for men who are growing increasingly follicly-challenged (BalderDash®), has the following demand function: Q = a + bP + cM + dR where Q is the quantity demanded of BalderDash’s most popular shoes, P is the price of that product, M is consumer income, and R is the price of a related product. The regression results are: Adjusted R Square 0.7796 Independent Variables Coefficients Standard Error t Stat P-value Intercept 21,055.04 1428.27 14.74 8.1E-16 P -83.912 19.079 -4.398 0.000 M 0.0266 0.013 2.064 0.047 R -16.6 10.664 -1.556 0.129 Discuss whether you think these regression results will generate good sales estimates for BalderDash. Now assume that the income is $69,100, the price of the related good is $39, and BalderDash chooses to set the price of its product at $54. b. What is the estimated number of units sold given the data above? (round to nearest unit; no decimals) c.…
A static model is postulated when a change in the independent variable at time T is believed to have an effect on the dependent variable at period Da change in the independent variable at time t'is believed to have an effect on the dependent varlable for al successive time periods. Ca change in the independent variable at time tis telieved to have an immediate effect on the dependent variable. da change in the independent variable at time T does not have any effect on the dependent variable
Consider the following model of house prices: lhprice=β0+β1bdrms+β2llotsize+β3lsqrft+u The variables are defined as: lhprice = the natural log of the house price bedrms = the number of bedrooms llotsize = the natural log of the land or lot size lsqrft = the natural log of the floor space of the house in square feet. You may assume the Gauss Markov Assumptions hold. Which of the following will make our inference on the partial effect of the number of bedrooms on the house price less precise? a) A smaller variance of any unobserved factors captured in the error term, u. b) A larger sample size. c) More variation in the number of bedrooms across the observations in the sample. d) None of the possible answers describe something that would make our inference less precise. e) A higher correlation between the number of bedrooms and the floor space of the house.
A researcher wants to assess the impact of school location on CSEC performances in Guyana. Suppose she categorises schools by Administrative Regions, how many dummy variables must be present in the model specification? The first difference of the logarithmic transformation is equivalent to a.The elasticity models b.The difference between a variable and its lag c.The growth rate of the series d.The autoregressive model
Hide student question Time Left : 01:58:26 Student question Analyze the model significance using the regression statistics: R-square, t-statistics, and use the standard error to determine the range within which demand for the product will fall with a 95% confidence interval. Calculate and interpret the following elasticities: Own price, cross price, income, and advertising and explain what these results suggest about the effects of changes in each of these variables on the demand for Maa mustard oil? Based on the elasticities you have estimated in question 8, what will be the impact of a price increase by the company on the total revenue of Maa mustard oil (assuming other variables remain constant)? What is the revenue maximizing price for Maa oil if the competitors price does not increase in October 2015? Solve pls
The following data from a random sample of individuals that are 25 years old. Note, wage is the hourly earnings in US dollars and educ is years of education. i 1 2 3 4 wage 12.18 12.18 20.09 7.39 educ 13 11 16 12 Consider the following model: In(wage)-Bo+B₁educ+u. Calculate the estimate of B₁. Round your answer to two decimal places.
An analyst working for your firm provided an estimated log-linear demand function based on the natural logarithm of the quantity sold, price, and the average income of consumers. Results are summarized in the following table: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept LN Price LN Income df 0.968 0.937 0.933 0.003 30 SS MS F 2 0.003637484 0.001818742 202.48598 0.000242516 8.98206E-06 27 29 0.00388 Coefficients Standard Error 0.57 0.00 0.13 0.51 -0.08 0.15 t Stat 0.90 -19.50 1.13 P-value 0.37 0.00 0.27 Significance F 5.55598E-17 Lower 95% -0.65 -0.09 -0.12 How would a 4 percent increase in income impact the demand for your product? Demand would increase by 60 percent. Demand would increase by 0.6 percent. Demand would decrease by 60 percent. Demand would decrease by 0.6 percent. Upper 95% 1.68 -0.07 0.41
The following model studies human welfare using the General Social Survey in the US: happy = 0.014 + 0.209regattend + 0.103occattend + 0.027income + 0.009educ +0.003 female where happy is a dummy variable equal to one if a person is happy, and 0 otherwise: regattend is a dummy variable equal to one if a person regularly attends church, and 0 otherwise: occattend is a dummy variable equal to one if a person occasionally attends church, and 0 otherwise; income is family income in tens of thousands: educ is years of schooling: female = 1 if female. = 0 otherwise Interpret the impact of religion on happiness.
Consider the following model of the factors that influence a person's annual wage: Y = β0 + β1X1 + β2X2 + β3X1X2 + β4X3 + u where Y = annual wage, in dollars X1= years of formal education X2 = age, in years X3 = variable equal to 1 if male, 0 otherwise U = error term Which of the following is the interpretation of β1 alone? The effect of X1 on Y when X2 = 0 The effect of X1 on Y when X2 is at its median value. The effect of X1 on Y, holding all else equal. The effect of X1 on Y when X2 is at its mean value.
Your company just became international by offering its products in both the United States and Canada. Experts in your analytics department believe that tastes for your product differ in those two countries, and have carefully collected data on prices and quantity demanded in both countries. They then present you with the results of two regressions, one for each country, as follows: Log Price regressed on Log Quantity (United States): Standard Coefficients Error t Stat P-value Lower 95% Upper 95 % 1.67605E- Intercept 52.75573994 10.81051303 4.88040363 31.48708283 74.0239705 06 | Log Quantity -5.382266173 1.170584108 -4.597932039 6.15253E-06 -7.685279168 -3.079253177 Log Price regressed on Log Quantity (Canada): Standard Error Coefficients 22.8707593 10.64507785 -2.095788278 | 1.152727409 -1.818112644 0.069981782 t Stat 2.148482109 Upper 95% 43.8139384 P-value Lower 95% Intercept Log Quantity 0.032425603 1.9275802 -4.363669916 0.17209336 Assume you have adequate statistical significance…
To determine whether the revenue made trom selling ice creams in a smal shop (in USD) and the temperature (in Celcius degrees) are related, a researcher decides to employ the simple linear regression analysis. Using a random set of days in which the temperature varies between 20 and 35, she comes up with the following regression line: Ice Cream Revenue- 200 90 Temperature Based on this analysis, what can she conclude? O A)At 20 Celcius degrees. the shop owner can xpect to sell 5000 USD O B)AI 30 Celoius degrees, the shop owner can expect to sel 2000 usD O C)As the temperature goes up by one degree Celoius, the revenue increases by approximately 00% OD)On average ce cream revenue is about 200 USD when the temperature is O degrees Celcus O E)As the temperature goes up by one degree Celcius. the reverue inereases by approximately 200 USD.