Using Multiple Regression in Excel for Predictive Analysis

Summary

This 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 variables—products A, B, and C—and 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

• 📊 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.
• 🔢 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.
• 📈 Linear regression is chosen for the predictive analysis due to the assumed linear relationship between the cost and the quantities of products produced.
• 🛠️ Before starting, the script emphasizes the need to ensure the regression tool is loaded in Excel, which can be done through the 'Analysis ToolPak'.
• 📝 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.
• 📋 The inclusion of column labels in the regression analysis is mentioned, which is important for clarity and reference in the output.
• 📉 The script highlights the importance of inspecting P-values to determine the significance of each independent variable in the predictive model.
• 🚫 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.
• ♻️ After evaluating the P-values, the script demonstrates how to rerun the regression analysis excluding the independent variable with a low predictive value.
• 🔑 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.
• 💡 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

00:00

📊 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.

05:02

🔍 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.

Keywords

💡Predictive Analysis

Predictive analysis is a process that involves using data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. In the video, predictive analysis is used to forecast the cost of running a factory based on the production of different products. The script describes setting up a predictive model using Excel for this purpose.

💡Excel

Excel is a widely used spreadsheet program that offers various functionalities, including data analysis and predictive modeling. The video script describes using Excel to perform linear regression for predictive analysis, highlighting its capabilities for business and financial forecasting.

💡Linear Regression

Linear regression is a statistical method for modeling the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. In the context of the video, linear regression is utilized to predict the cost of running a factory based on the quantities of products A, B, and C produced.

💡Independent Variables

In the context of regression analysis, independent variables are the factors that are believed to influence the dependent variable. In the script, products A, B, and C are the independent variables that are thought to affect the cost of running the factory.

💡Dependent Variable

A dependent variable is the outcome or the event that is being predicted or explained by the independent variables. In the video, the cost of running the factory is the dependent variable, which is predicted based on the quantities of products A, B, and C produced.

💡Factory Cost

Factory cost refers to the expenses incurred in operating a manufacturing facility. In the video, the script discusses predicting the factory cost using a predictive model that takes into account the production quantities of different products.

💡Data Collection

Data collection is the process of gathering and measuring information from various sources to obtain data for analysis. The script mentions that data was collected over 19 months, which is then used for the predictive analysis of the factory's cost.

💡Regression Equation

A regression equation is the mathematical formula used in regression analysis to describe the relationship between variables. The script explains that the regression equation in the model represents a straight line, which is used to predict the factory cost based on the quantities of products produced.

💡Coefficients

In regression analysis, coefficients are numerical values that represent the relationship between the independent and dependent variables. The script discusses the coefficients for products B and C, which are used in the regression equation to predict the factory cost.

💡P-Values

P-values are used in statistical hypothesis testing to determine the probability that the observed results occurred by chance. In the video, P-values are inspected to assess the significance of the independent variables in predicting the factory cost, with a threshold of 0.15 used to exclude variables that are not significant.

💡Analysis ToolPak

The Analysis ToolPak is an add-in for Excel that provides additional statistical and analysis tools, including regression analysis. The script describes how to load the Analysis ToolPak in Excel to perform the predictive analysis.

💡Predictive Value

Predictive value in the context of statistical analysis refers to the usefulness or significance of a variable in predicting outcomes. The script explains that variables with P-values greater than 0.15 are considered to have low predictive value and are excluded from the model.

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