The researcher needs to create a regression equation specification based on the data the researcher has collected according to the table above. In detail, the data that has been collected can be seen in the table below: The data collected includes product sales measured in units and prices measured in thousand USD per unit. Therefore, product sales are created as the dependent variable and product prices are created as the independent variable.įor example, researchers have collected annual time series data from 2011 to 2020. In this case study, it is estimated that product prices affect product sales. Here, researchers can collect time series data consisting of product sales and price variables. Therefore, product price data is needed because the goal is to determine product sales predictions. On this occasion, Kanda Data will discuss product sales prediction estimates based on a simple linear regression equation. Several approaches can be used to predict product sales using regression. Product sales predictions can be estimated using linear regression. Predict product sales using linear regression Furthermore, researchers can enter the value of the price variable, and then product sales predictions can be obtained. The accuracy and precision of the product sales prediction depend on the P-value and the coefficient of determination of the equation created. Based on the regression estimation equation, it can be used to predict product sales in the next period. The intercept value and the estimated coefficient of the price variable are obtained based on the estimation results using time series data.įurthermore, using the estimation from the linear regression analysis, we can construct the regression estimation equation. Estimating the regression equation based on empirical data owned by the company can be used to predict product sales the next time.įor example, a company aims to observe the effect of price on product sales. We will start with determining the P-value for tail 1 or in one direction.In business management, linear regression can be used to predict sales. In this section, we will be using the T.TEST function to determine the P values for tails 1 and 2. Method-2: Using T.TEST Function to Calculate P Value in Linear Regression in Excel Moreover, we can see that for the Alpha value of 0.05 we are getting the P values less than 0.05 which means it neglects the null hypothesis and so the data is highly significant. Because the two-tail P-value considers both the increase and decrease of the marks whereas the one-tail P-value considers only one of these cases. We can see the one-tail P-value is half times the two-tail P-value. ➤ You can change the value for Alpha from 0.05 (automatically generated) to 0.01 because the designated value for this constant is generally 0.05 or 0.01.Īfter that, you will get the P-value for two cases the one-tail value is 0.00059568 and the two-tail value is 0.0011913. ➤ As Input we have to provide two variable ranges $C$4:$C$11 for Variable 1 Range and $D$4:$D$11 for Variable 2 Range, as Output Range we have selected $E$4. ➤ Select the option t-Test: Paired Two Sample for Means from different options of Analysis Tools.Īfter that, the t-Test: Paired Two Sample for Means dialog box will open up. Then, the Data Analysis wizard will appear. ➤ Now, go to the Data Tab > Analysis Group > Data Analysis Option. ➤ Check the Analysis ToolPak option and press OK. ➤ Choose the Excel Add-ins option in the Manage box and then press Go.Īfterward, the Add-ins dialog box will pop up. ➤ Select the Add-ins option on the left panel. If you didn’t activate the data analysis tool then first enable this toolpak at first.Īfter that, the Excel Options dialog box will appear. Here, we will use the analysis toolpak containing the t-Test analysis tool to determine the P-value for these two sets of sales data. Method-1: Using ‘t-Test Analysis Tool’ to Calculate P Value We have used Microsoft Office 365 version here, you can use any other versions according to your convenience. The null hypothesis reckons there is no difference between the two types of sales values and the alternative hypothesis will consider differences between these two sets of values. We will compare these sales values and determine the probability value and then we will determine if P supports the null hypothesis or the alternative hypothesis. Here, we have some predicted sales values and actual sales values of some of the products of a company. Related Articles How to Calculate P Value in Linear Regression in Excel: 3 Ways
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