Carefully review the slides in pages 22 ~ 39 of W7(Period2), and do the followings in Radiant and create a report in a word document ?1. Visual
Carefully review the slides in pages 22 ~ 39 of W7(Period2), and do the followings in Radiant and create a report in a word document
1. Visualization
◦Obtain scatter plots of Sales vs. Price, and Sales vs. Ads, and explain the relationship from your visual inspection of data
◦Obtain bar plots of Sales by Thanksgiving and Christmas, and explain and interpret the differences
2. Effects of control variables
◦Regress Sales on Price, then interpret the coefficient
◦Regress Sales on Price, Thanksgiving, then explain how coefficient changes
◦Regress Sales on Price, Thanksgiving, Christmas then explain how coefficient changes
◦Repeat this for Ad Spend instead of Price
3. Regression analysis
◦Regress Sales on Price, Ad Spend, Thanksgiving, and Christmas
◦Interpret all four coefficients
Objectives Overview of regression analysis
Understanding control variables
Overview of Radiant
2
Regression How is the outcome variable associated with the explanatory variable(s)
Sales ($)
Advertising ($1,000)
3
Regression How is the outcome variable associated with the explanatory variable(s)
Sales ($)
Advertising ($1,000)
4
Regression
How is the outcome variable associated with the explanatory variable(s)
Sales ($)
Advertising ($1,000)
����� = � + � * ����������� + �
5
Regression How is the outcome variable associated with the explanatory variable(s)
Sales ($) �
�
�
Advertising ($1,000)
����� = � + � * ����������� + �
6
Regression How is the outcome variable associated with the explanatory variable(s)
Sales ($) !
#
"
Advertising ($1,000)
����� = � + � * ����������� + �
Intercept: Baseline sales in $ when advertising expenditure is zero
7
Regression How is the outcome variable associated with the explanatory variable(s)
Sales ($) !
#
"
Advertising ($1,000)
����� = � + � * ����������� + �
Slope: If you increase 1 unit of advertising (in $1,000),
you expect sales increase by $ �
8
Regression How is the outcome variable associated with the explanatory variable(s)
Sales ($) !
#
"
Advertising ($1,000)
����� = � + � * ����������� + �
Error: Individual observation’s deviation from the average
tendency, where the average tendency is summarized by � and �
9
Regression How is the outcome variable associated with the explanatory variable(s)
Sales ($) !
#
"
Advertising ($1,000)
����� = � + � * ����������� + �
Error: Individual observation’s deviation from the average tendency, where the
average tendency is summarized by � and �
Given Sales and Advertising observations, regression analysis summarizes the pattern by finding � and � that minimizes overall errors
10
Control variables What if the data include observations during Thanksgiving break, such as black Friday and cyber Monday?
Sales ($)
Advertising ($1,000)
11
Control variables What if the data include observations during Thanksgiving break, such as black Friday and cyber Monday?
Sales ($)
Advertising ($1,000)
12
Control variables What if the data include observations during Thanksgiving break, such
as black Friday and cyber Monday?
Sales ($) Thanksgiving!
Advertising ($1,000)
Can we say the advertising was effective? Aren’t we overconfident? 13
Control variables What if the data include observations during Thanksgiving break, such
as black Friday and cyber Monday?
Sales ($) Thanksgiving!
Advertising ($1,000)
����� = � + � * ����������� + � * � + �
14
Control variables
What if the data include observations during Thanksgiving break, such as black Friday and cyber Monday? – Base sales is $� for Thanksgiving
Sales ($) Thanksgiving!
�
Control variable Z = 1 for Thanksgiving, 0 otherwise
Advertising ($1,000)
����� = � + � * ����������� + � * � + �
15
Control variables What if the data include observations during Thanksgiving break, such as black Friday and cyber Monday? – Base sales is $� for Thanksgiving
Sales ($) Thanksgiving!
�
Control variable Z = 1 for Thanksgiving, 0 otherwise
Advertising ($1,000)
����� − � * � = � + � * ����������� + �
16
Control variables What if the data include observations during Thanksgiving break, such as black Friday and cyber Monday?
Control
variable
Sales ($)
Z = 1 for Thanksgiving, 0 otherwise
Advertising ($1,000)
����� − � * � = � + � * ����������� + �
17
Control variables What if the data include observations during Thanksgiving break, such as black Friday and cyber Monday?
Control
variable
Sales ($)
Z = 1 for Thanksgiving, 0 otherwise
Advertising ($1,000)
����� − � * � = � + � * ����������� + �
18
Control variables What if the data include observations during Thanksgiving break, such as black Friday and cyber Monday? – Overconfident in ad spend!
Control
variable
Sales ($)
Z = 1 for Thanksgiving, 0 otherwise
Advertising ($1,000)
����� − � * � = � + � * ����������� + �
19
Control variables Without the Thanksgiving control variable, we misunderstood that advertising effectiveness is higher than the truth
That was baseline sales increase due to the Thanksgiving shopping season, not the effects of advertising
We can consider various controls for baselines depending on the information you have:
◦ Industry ◦ Brand ◦ Seasonality ◦ Months ◦ Day of the week ◦ Time of the day ◦ And many more!
20
Multiple regressions
We can expand it with multiple explanatory
variables
����� = � + �!" * ����������� + �# * ����� + � * � + �
We can also expand it with multiple control variables
����� = � + �!" * ����������� + �# * ����� + �$% * �$% + �&'()* * �&'()* + �
21
Now let’s do it Make sure to be ready to use Radiant using one of the three options: 1. Install on your computer: https://radiant-rstats.github.io/docs/install.html
2. Log on to the virtual lab at http://ucr.apporto.com/ using your UCR Net ID and remotely work on Radiant from the virtual lab
◦ Instructions for the virtual lab is here: https://ucrsupport.service now.com/ucr_portal/?id=kb_article&sys_id=8b5964291b84d49026bd635bbc4bcbd7
◦ In case that you have trouble working on the virtual lab, you will need to directly contact the IT office
3. (Emergency protocol, but not recommended) Use online version of Radiant at https://vnijs.shinyapps.io/radiant/
◦ Functionality is limited, and security is a concern
Download your sample dataset from eLearn, Course Materials 22
Radiant First run “Rstudio”
23
Radiant Click “Addins”
24
Radiant Click “Start radiant (browser)”, then Radiant will show up on your web browser
25
Radiant On the left panel, click the drop down menu under “Load data of type”, then choose “csv”
26
Radiant Click “Load”, then select the folder you stored the data file. Select the
file (sample_data_reg.csv) then click “Select”
27
Data There are 5 columns, 100 rows in the dataset ◦ Sales (in $1,000): Outcome variable ◦ Price (in $): Explanatory variable ◦ Ad spend (in $1,000): Explanatory variable ◦ Thanksgiving: Control variable ◦ Christmas: Control variable
First 10 rows are from Thanksgiving
Second 10 rows are from Christmas
Remaining 80 rows are from “off-season”
28
Data Control variables need to be transformed from “numbers” to “factors”
1. Click “Transform” on top 2.
Select “Thanksgiving” under
“Select variables”
3. Select “Change type” under “Transformation type”
4. Select “As factor” under “Change variable type”
5. Click “+Store”
Repeat this for “Christmas” 1
2
3
4
5
29
Visualization First, we can visually inspect data 1. Click “Visualize” on top
2. Select “Scatter” under “Plot-type”
3. Select “Sales_in_1_000” as “Y
1
variable” 5
4. Select “Price_in_” as “X-variable” 2
5. Click “Create plot”
3
How do sales change as prices increase or decrease?
4
30
Visualization First, we can visually inspect data
1. Click “Visualize” on top
2. Select “Scatter” under “Plot-type”
3. Select “Sales_in_1_000” as “Y variable”
4. Select “Price_in_” as “X-variable”
5. Click “Create plot”
Repeat these five steps for
“AdSpend_in_1_000”
How do sales change as ad spend increases or decreases?
31
Visualization We can also inspect seasonality
1. Select “Bar” under “Plot-type”
2. Select “Sales_in_1_000” as “Y variable”
3. Select “Thanksgiving” as “X- 4 variable”
4. Click “Create plot”
1
2
There is about $10K difference in sales between Thanksgiving and
3
others on average
Repeat these four steps for “Christmas”
32
Regression analysis Click “Model” on top menu, and select “Linear
regression”
33
Regression analysis: Step 1 Select “Sales_in_1_000” as “Response variable”
Select “Price_in_” as “Explanatory variables”
Click “Estimate model”
Coefficient for “Price_in_” (i.e., �!) is -0.499 ◦ What does it mean? ◦ Unit increase in price is associated with …
34
Regression analysis: Step 2
Select “Sales_in_1_000” as “Response variable”
Select “Price_in_” as “Explanatory variables”
Add “Thanksgiving” to the “Explanatory variables” ◦ Ctrl + Click multiple variables
Click “Re-estimate model”
How did coefficient for “Price_in_” change? ◦ What does it mean?
35
Regression analysis: Step 3 Select “Sales_in_1_000” as “Response variable”
Select “Price_in_” as “Explanatory variables”
Add “Thanksgiving” and “Christmas” to the “Explanatory variables” ◦ Ctrl + Click multiple variables
Click “Re-estimate model”
How did coefficient for “Price_in_” change? ◦ What does it mean?
36
Regression analysis: Step 3 Select “Sales_in_1_000” as “Response variable”
Select “Price_in_” as “Explanatory variables”
Add “Thanksgiving” and “Christmas” to the “Explanatory variables” ◦ Ctrl + Click multiple variables
Click “Re-estimate model”
How did coefficient for “Price_in_”
change? ◦ What does it mean?
Repeat the three steps for “Ad_Spend” ◦ How does the ad effectiveness estimate
change by adding control variables?
37
Regression analysis: Full model Select “Sales_in_1_000” as “Response variable”
Select “Price_in_” and “Ad_Spend” as “Explanatory variables” Add “Thanksgiving” and “Christmas” to the “Explanatory variables” Click
“Re-estimate model” 38
Interpretation, on average $1 increase in price is associated with …
$1,000 increase in ad spend is associated with …
Baseline sales in Thanksgiving are higher than those in off season by..
Baseline sales in Christmas are higher than those in off season by..
39
Assignment 7: Due May 16th Carefully review the slides in pages 22 ~ 39
1. Visualization ◦ Obtain scatter plots of Sales vs. Price, and Sales vs. Ads, and explain the
relationship from your visual inspection of data
◦ Obtain bar plots of Sales by Thanksgiving and Christmas, and explain and interpret the differences
2. Effects of control variables ◦ Regress Sales on Price, then interpret the coefficient ◦ Regress Sales on Price, Thanksgiving, then explain how coefficient changes ◦ Regress Sales on Price, Thanksgiving, Christmas then explain how coefficient changes
◦ Repeat this for Ad Spend instead of Price
3. Regression analysis ◦ Regress Sales on Price, Ad Spend, Thanksgiving, and Christmas ◦ Interpret all four coefficients
40
Collepals.com Plagiarism Free Papers
Are you looking for custom essay writing service or even dissertation writing services? Just request for our write my paper service, and we'll match you with the best essay writer in your subject! With an exceptional team of professional academic experts in a wide range of subjects, we can guarantee you an unrivaled quality of custom-written papers.
Get ZERO PLAGIARISM, HUMAN WRITTEN ESSAYS
Why Hire Collepals.com writers to do your paper?
Quality- We are experienced and have access to ample research materials.
We write plagiarism Free Content
Confidential- We never share or sell your personal information to third parties.
Support-Chat with us today! We are always waiting to answer all your questions.
