![]() ![]() We can write a formula in excel by typing =SUM(…). ![]() To obtain the sigma value, we only need to perform the addition operation for all observations consisting of the Y variable, X variable, X square, and XY. Next, copy-paste the excel formula for the rest of the following observation data until it’s finished. ![]() This calculation is done by multiplying the first observation data for the bread sales variable (Y) and the selling price variable (X). The next step is to copy-paste the excel formula for the X square value from the second observation to last. We calculate the X square for the first observation by writing the formula =X^2 in excel. Our first step is to calculate the value of the X square. We can arrange the template as below:Ĭalculating X square is relatively easy to do. In addition to adding X square and XY in the excel template, we need to add a Sum (sigma) row. To make it easy for us to operate, we can make a template first using excel. We need to pay attention to calculating each component needed based on the sample observations that we use. The sigma sign in the formula means that we must operate first for all variables, then add up the values. The formula used, I refer to the formula in the book written by Koutsoyiannis (1977) as follows:īased on the above formula, we can choose whether to calculate the intercept value (bo) first or b1 first. We need to do the calculations based on the bo and b1 calculation formulas. In calculating the coefficients bo and b1 in this article, they will be calculated using the original sample observation method. The data for which the bo and b1 values will be calculated can be seen in the table below: You can practice using this data, or if you already have your data to process, it will be even better. For convenience, here I will convey the data that we will use. In this article, we will calculate the intercept (bo) value and the estimated value of the coefficient of the independent variable (b1). The specifications of the model, in detail, we can arrange as follows:īased on the regression equation above, it means that we have compiled a model specification for a simple linear regression that we will calculate. Furthermore, we can draw up a model specification based on the data collected. Bread sales as the affected variable is called the dependent variable (given the symbol Y). Thus, selling price as a variable that influences us is called the independent variable (given the symbol X). Based on the case study presented in the previous paragraph, this research aims to find out how the selling price influences bread sales. The first thing we need to arrange is to determine the dependent and independent variables. Bread sales are measured in thousand pieces, and Selling price is measured in USD/unit. The data used is annual time series data from 2010 to 2019. ![]() This article will give an example of a case study on the effect of selling price on bread sales. In simple linear regression, we need to know that the number of variables used only consists of one dependent variable and one independent variable. I will convey the second calculation method in the following article. In this article, the calculation of the regression estimation coefficient will be calculated using sample observation (the first method of calculation). The tutorial will be discussed in several parts, where this time we will calculate together the estimated regression coefficients. Did you know it turns out that doing simple linear regression calculations can be done easily? Manual linear regression calculations can be completed using a calculator or excel.īecause there are still many who need a tutorial on how to calculate simple linear regression manually, on this occasion, I will discuss a tutorial on how to calculate simple linear regression using Excel manually. Calculating manually simple linear regression becomes essential, especially for researchers or students deepening econometrics or statistics. In conducting data analysis, we not only need to know how to analyze and interpret the results, but we also need to understand how to calculate manually. ![]()
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