Data Analysis and Decision Modelling for Predicting Share Price of Werner Enterprises, Inc.

Running head: DATA ANALYSIS AND DECISION MODELLING 1
Data Analysis and Decision Modelling
Predicting the Share Price of Werner Enterprises, Inc.
Student Name
University Name

DATA ANALYSIS AND DECISION MODELLING 2
Contents
1.0 Executive Summary.................................................................................................................3
2. 0 Description of Data.................................................................................................................3
3.0 Variance Inflation Factor (VIF).............................................................................................5
4.0 Residual Analysis.....................................................................................................................5
5.0 Analysis of Variance (ANOVA) Table...................................................................................7
6.0 Coefficient of Determination (R-Squared)............................................................................8
7.0 Hypothesis Tests on Each Input.............................................................................................8
8.0 Coefficients...............................................................................................................................9
9.0 Prediction of Tomorrow’s Share Price................................................................................10
10.0 Conclusion............................................................................................................................11
11.0 Appendix –VIF Values........................................................................................................12
12. 0 References............................................................................................................................13

DATA ANALYSIS AND DECISION MODELLING 3
1.0 Executive Summary
The objective of this report is to examine whether historical data can be utilized in
the prediction of the future change in the prices of the Werner Enterprises, Inc. The
enterprise is a transportation and logistics company that was established on 14th,
September, 1982. It engages in interstate and intrastate commerce on transporting of
truckload shipments of general commodities and carries out its operations through two
major segments namely: Truckloads and Werner Logistics. To meet the objectives
analysis is done on company’s data collected between 2014 and 2016. Focus is laid on
the relationship between the day’s price variation and the next day’s variation on the
share price for the company. Multiple linear regression including analysis of variance,
coefficient of determination, residues and Variance Inflation Factor has been performed
to reach a conclusive decision.
2. 0 Description of Data
The data is collected is for 382 days between 2014 and 2016. The data has twelve
variables; the first variable is date, the other ten are used as independent variables while
the twelve variable is used as the dependent variable. The variables indicate the variations
in prices of financial assets for the days on which the data was collected. Some of the
variables and their purposes are listed below:
 Date: Indicating the year upon which the data was collected
 Aluminum_Vel1: Indicating change in prices of aluminum backdated by a
day
 Copper_Vel1: Indicating change in prices of copper backdated by a day

DATA ANALYSIS AND DECISION MODELLING 4
 US_Gasoline_Vel1: Indicating change in prices of Gasoline backdated by
a day.
 West_Texas_Vel1: Indicating change in price of West Texas Intermediate
Oil by backdated by a day.
 SPDR_XL1: Indicating the U.S. industrial confidence for industrial-
oriented firms.
 CA-Dollar_Vel1: The exchange rate between U.S. and Canadian dollar
backdated by a day.
 SP 500: The standard and Poor’s 500 index of stock prices.
The data also consists of interaction variables which the product of two variables to
create new variables. These variables are:
 Year x WERN
 30year x Copper_Vel1
 Aluminum_Vel1 x Aluminum_Vel1
 Aluminum_Vel1 x West_Texas_Vel1
 Baltic_Vel1 x Copper_Vel1
 SPDR_XL1 x West_Texas_Vel1
The variable contained in the last column of the data is the dependent variable that
needs to be predicted. It has been sorted and ranked with the initial percentage variations
in price divided by the total number of rows in the data so that they vary from 0 to 1.
Zero is the maximum decrease in price, the median 0.5 indicates no change in price and 1
indicates the maximum increase in price.