Using Multiple Regression in Excel for Predictive Analysis
Summary
TLDRThis script offers a tutorial on using Excel for predictive analysis in a factory setting. It covers setting up a linear regression model with three independent variablesβproducts A, B, and Cβand one dependent variable, the factory cost. The process includes loading the Analysis ToolPak, performing regression analysis, interpreting P-values to determine variable significance, and adjusting the model by excluding non-significant variables. The end goal is to predict factory costs based on product quantities, demonstrating practical application of multiple linear regression.
Takeaways
- π The video discusses using Excel for predictive analysis with a focus on setting up a predictive model for a factory that produces three types of products.
- π’ The cost of running the factory is the dependent variable, which is influenced by the quantities of products A, B, and C produced, serving as the independent variables.
- π Linear regression is chosen for the predictive analysis due to the assumed linear relationship between the cost and the quantities of products produced.
- π οΈ Before starting, the script emphasizes the need to ensure the regression tool is loaded in Excel, which can be done through the 'Analysis ToolPak'.
- π The script provides a step-by-step guide on how to perform multiple linear regression in Excel, starting with selecting the Y range for the dependent variable and the X range for the independent variables.
- π The inclusion of column labels in the regression analysis is mentioned, which is important for clarity and reference in the output.
- π The script highlights the importance of inspecting P-values to determine the significance of each independent variable in the predictive model.
- π« It is noted that any independent variable with a P-value of 0.15 or greater should be excluded from the model as it does not significantly contribute to predicting outcomes.
- β»οΈ After evaluating the P-values, the script demonstrates how to rerun the regression analysis excluding the independent variable with a low predictive value.
- π The final step involves using the coefficients from the regression analysis to plug into a formula to make predictions about the factory cost based on given quantities of products.
- π‘ The video concludes with an example prediction, demonstrating how to apply the regression formula to estimate the cost of running the factory for specific product quantities.
Q & A
What is the purpose of using Excel for predictive analysis in the given scenario?
-The purpose is to set up a predictive model for a factory that produces three types of products (A, B, and C) to analyze how the quantity of each product affects the cost of running the factory.
What is the dependent variable in the predictive model discussed in the script?
-The dependent variable is the cost of running the factory, which is what the model aims to predict based on the quantities of products produced.
What are the independent variables in the predictive model?
-The independent variables are the quantities of the three products (A, B, and C) produced in the factory.
What statistical method is being used for the predictive analysis in the script?
-The method being used is multiple linear regression, which allows for the prediction of a dependent variable based on multiple independent variables.
How many months of data has been collected for the predictive analysis?
-The data collected spans over the course of 19 months.
What is the significance of the P-value in the context of this predictive analysis?
-The P-value is used to determine the significance of each independent variable in predicting the outcome. Variables with P-values of 0.15 or greater are considered not significant and are excluded from the model.
Why was the independent variable for product A excluded from the model?
-Product A was excluded because its P-value was 0.23, which is greater than the threshold of 0.15, indicating it is not significant in predicting the cost of running the factory.
What is the intercept in the context of the regression equation?
-The intercept is the constant term in the regression equation, representing the expected value of the dependent variable when all independent variables are zero.
How does Excel assist in performing the multiple linear regression?
-Excel provides a 'Data Analysis' tool with a 'Regression' function that automates the process of running a regression analysis and provides the coefficients and intercept for the model.
What is the final step in using the regression model to make a prediction?
-The final step is to plug the coefficients and intercept from the regression output into the regression equation, along with the values of the independent variables, to calculate the predicted cost of running the factory.
How does the script illustrate the application of the regression model to predict factory costs?
-The script provides an example where the model is used to predict the monthly cost for producing specific quantities of products B and C, excluding product A due to its low predictive value.
Outlines
π Setting Up a Predictive Analysis Model in Excel
This paragraph introduces the process of using Excel for predictive analysis in a factory setting. The factory produces three types of products, and the cost of operation depends on the quantity of each product produced. Over 19 months of data has been collected to establish a predictive model using linear regression. The independent variables are the three products (A, B, and C), and the dependent variable is the factory cost. The paragraph explains the initial steps of ensuring the regression tool is available in Excel and how to access it through the Data tab. It also details the process of setting up the regression analysis by selecting the dependent and independent variables, including column labels, and establishing an output range for the results.
π Evaluating Predictive Value and Rerunning Regression
The second paragraph delves into the analysis of P-values to determine the significance of independent variables in the predictive model. It explains that a P-value greater than 0.15 indicates the variable's predictive value is not significant and should be excluded from the model. The paragraph provides an example where Product A's P-value is too high, leading to its exclusion from the model. The process of rerunning the regression analysis with only the significant independent variables (Product B and C) is described, resulting in stronger predictive values for the remaining variables. The paragraph concludes with the method of using the regression coefficients to make predictions about the factory's monthly cost based on the production quantities of the products.
Mindmap
Keywords
π‘Predictive Analysis
π‘Excel
π‘Linear Regression
π‘Independent Variables
π‘Dependent Variable
π‘Factory Cost
π‘Data Collection
π‘Regression Equation
π‘Coefficients
π‘P-Values
π‘Analysis ToolPak
π‘Predictive Value
Highlights
Using Excel for predictive analysis in a factory setting.
Setting up a predictive model for three types of products: A, B, and C.
The cost of running the factory is dependent on the quantity of products made.
Data collected over 19 months to be used for analysis.
Utilizing linear regression for predictive analysis.
Multiple independent variables in the model: products A, B, and C.
The dependent variable is the cost of running the factory.
Assumption of a linear relationship for the regression model.
Ensuring the regression tool is loaded in Excel.
Accessing the Data Analysis toolpack in Excel.
Using the Regression function within Data Analysis.
Selecting the Y range for the dependent variable: factory cost.
Identifying the X range with multiple independent variables.
Including column labels in the regression analysis.
Excel performing most of the regression analysis work.
Inspecting P values to determine significance of variables.
Excluding independent variable A due to a high P value.
Rerunning regression excluding variable A for better predictive values.
Using coefficients to plug into the formula for prediction.
Predicting the monthly cost for given product quantities.
Final prediction of factory cost using multiple linear regression.
Transcripts
we're going to look at using Excel to do
some predictive analysis uh we're going
to set up a predictive model for our
Factory and in our Factory we make three
types of products uh a b and c and
depending on the quantity of each of
those products depends uh is dependent
upon how much it costs to run the
factory
so we've collected this data over the
course of 19 months and we're going to
use linear regression to do the
predictive analysis now in linear
regression we have
multiple independent variables and the
independent variables that are in this
model are the three products we create
in our Factory a b and c those are the
independent variables what's the
dependent VAR variable what is dependent
upon those three variables is the cost
of running the factory so the dependent
variable in this case is the factory
cost so when you look at the regression
equation the multiple regression
equation you will see that uh what we
have is uh an equation for a straight
line and we can assume in this example
and all the examples in this course Mis
204 that there is a linear relationship
so that's why we're using an equation
for a line so where do we begin in
building our predictive analysis model
well first of all we need to make sure
that the regression tool is loaded and
we go click on data in the
tab and when we click on data we see
data analysis is loaded well how did we
get that
loaded go over to file
click
options click add
inss choose analysis tool
pack click
go make sure analysis tool pack is
checked we're not using solver in this
particular instance so we can uncheck
that click
okay and the tool pack is loaded so
let's click on data
analysis and when we click on data
analysis we have all of these different
statistical functions but the one that
we want to use for predictive analysis
is
regression so click on regression
okay now this dialogue box comes up and
it's very simple to do uh multiple
linear regression in Excel the first
thing we want to do is we want to uh
select the Y range and the Y range is
our dependent
variable so we'll select our dependent
variable cost is dependent upon the
products that are made in our
Factory the X range we have multiple
independent
variables in this case we have X1 2 and
3 denoted by products a b and
c
perfect and if you noticed I've included
the column
labels and uh we're going to have the
output range let's put the output range
on the same sheet so we can compare side
by side the different outcomes and we'll
put the output range right there well
let's move it over one so we don't
interfere with our
formula and uh that's it we'll click
okay and Excel does most of the work for
us we see down at the bottom we have the
intercept and The Intercept will be the
constant in our
formula and we have the coefficients for
each of our independent
variables which are listed there but
before we do anything remember we always
need to inspect the P values or the
predictive values of each of our
independent variables and any predictive
value that is 0.15 or greater we're
going to exclude 0.15 or greater says
that the predictive value of that
particular independent variable is such
that it really doesn't matter in
predicting our outcomes it is not of
significance so when we look at the P
values we see for product a the P value
is 023 which obviously is greater than
0.5
0.15 so we will
exclude we will exclude
using the values for the independent
variable for product a product B and
product C both have P values well below
0.15 so their predictive value is much
much greater so at this point we need to
rerun the AG regression excluding
independent variable a so let's go ahead
and do do
that going to go up here run the
regression one more
time and instead of selecting a b and c
this time we're only going to select B
and C why because we evaluated the P
values and found that A's P value the
predictive value was just simply too low
so there we go we're going to run it
again we're going to put it on the same
work work sheeet let's put it uh down
here right below
it and let's click
okay and we'll scroll down and look look
at the P values now that we've gotten
rid of independent variable a the P
values the predictive values of the
other independent variables have fallen
significantly and they have very very
strong predictive values
so at this point all we need to do is
take the different coefficients plug it
into the formula and do our prediction
so how do we do that well first of all
let's look at the line formula the
constant is the
intercept the B and X the B values is
what we're looking for so in this case
we are predicting the monthly cost for
our Factory if we make 1,200 model A's
800 model B's and a th000 model
C's well remember we excluded a so we
don't have to include that in our
formula so let's go write our formula
plugging in the v variables and predict
our cost so again beginning each Excel
formula with the equal sign equals the
constant and I'm reading left to right
here the constant which is the line
intercept
plus I'm excluding a because the P value
was too
low
800
times the
coefficient
plus a
th000 times
coefficient for
C and that's it so let's press enter and
make our
prediction and there it is we can
predict
that if we made given on given our
historical data from 19 months that if
we made 1 12200 model A's 800 model B's
and 1,000 model C these it will cost
$4,149 21 to run our
Factory there it is using multiple
linear regression to do predictive
analysis
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