Managerial Economics - Pre Task - Week:1 by https://padlet.com/dsubramani/4d02cv6f8jv6av1h en-us 2021-10-08 10:30:59 UTC 2021-10-08 12:30:05 UTC hello@padlet.com Briefly explain the meaning of the F-test. Why do you think this test is considered be more important in multiple regression analysis than it is in simple regression analysis? dsubramani https://padlet.com/dsubramani/4d02cv6f8jv6av1h/wish/1802211222 In general, an F-test in regression compares the fits of different linear models. Unlike T-test that can assess only one regression coefficient at a time, the F-test can assess multiple coefficients simultaneously. An F statistic is a value you get when you run an ANOVA test or a regression analysis to find out if the means between two populations are significantly different. 

F= Variation between sample means/Variation within the samples

It’s like a T statistic from a T-Test; A T-test will tell you if a single variable is statistically significant and an F test will tell you if a group of variables are jointly significant.
]]> 2021-10-08 10:31:24 UTC https://padlet.com/dsubramani/4d02cv6f8jv6av1h/wish/1802211222 Briefly explain the meaning of R2. A time series analysis of demand tends to result in a higher R2 than one using cross-sectional data. What do you think this is the case? dsubramani https://padlet.com/dsubramani/4d02cv6f8jv6av1h/wish/1802215956 The goodness of fit of a model is measured by R-squared. 



R-Squared = Explained variation/Total Variation

R-Squared is always between 0 and 100%.

A time series analysis of demand produces a higher R-squared than a cross-sectional analysis because data is collected at multiple period in time as opposed to one period in time when using cross-sectional data and also with time series analysis, the dependent variable of the time is always dependent on the independent variable. Since this is the case, R2 always is closer to 1. This is not the case with cross-sectional data and that can result in various values for R2.
]]> 2021-10-08 10:33:16 UTC https://padlet.com/dsubramani/4d02cv6f8jv6av1h/wish/1802215956 Explain the difference between time series data and cross-sectional data. Provide examples of each type of data. dsubramani https://padlet.com/dsubramani/4d02cv6f8jv6av1h/wish/1802216494 Time series data focuses on the same variable over time, whereas cross sectional data focuses on multiple variables at the same time. Furthermore, time series data are observations of a single subject at multiple time intervals, whereas cross sectional data are observations of many subjects at the same time.

Example:

Time Series: The daily closing price of a certain stock recorded over the last six weeks. Weekly sales figures of ice cream sold during a holiday period at Merseyside.
Cross Sectional: Temperature and precipitation of multiple cities in a same day.
inventory of all ice creams in stock at a particular store.
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