Econometrics Project of LG Electronics - Case Study Example

Econometrics Project Name Tutor Code Date Table of Contents Executive Summary 2 Company Background 4 Variables 4 Model 5 Sensitivity Analysis 7 Implications to Business Policy 8 References 9 Appendix 10 Executive Summary LG Electronics is a leading multinational company in home appliances, consumer electronics and mobile communications. The paper focuses on the sale of grills (dependent variable) and the factors affect the quantity demanded of grills (independent variables). The independent variables are: price, advertising expenses, gross domestic product, and income level. Through a multivariate regression analysis, the findings indicate that income level, advertising, and GDP positively affect the quantity demanded for grills. However, price negatively affects the quantity demanded for grills. Company Background LG Electronics is a leading multinational company in home appliances, consumer electronics and mobile communications. LG has four separate business divisions: Mobile Communications, Home Appliances and Air Solution, Home Entertainment, and Vehicle Components. The company is a global leader in the manufacture of mobile devices, flat panel televisions, washing machines, grills, air conditioners, and refrigerators. The company’s global operations are spread over 125 locations and employs over 70000 people. In 2015 the company realised $48.8 billion in total sales revenue. In this paper focus will be on the sale of grills (dependent variable) and what factors affect the quantity demanded of grills (independent variables). Variables One dependent variable and four independent variables are used in this study. Sales is the dependent variable. The independent variables include; price, income, advertising, and gross domestic product. Sales: The number of grills sold in a particular quarter. The units sold is influenced by the independent variables discussed below. Price: The price of grills produced by LG. Price is a major independent variable considered in the model because it influences the demand as well as the supply of the product. Income: This is also a crucial independent variable since it determines the average household spending power. Therefore, income represents the people’s purchasing power, which is indispensable in forecasting the sales efficiently. Advertising: Ads are an important element in business in the current business environment that is characterised by intense competition and constantly changing business conditions. A lot of companies today spend a dear sum of money to promote their products. Advertising is therefore an important independent variable in the model since it is the main factor expected to influence the people’s desire to buy a particular product as it can attract customers or change and maintain brand loyalty. Gross Domestic Product (GDP): The GDP is an important indicator of the overall economic status of the geographic region within which the product is sold. It represents the market value of all the finished goods produced in a nation. LG is a leading global electronics conglomerate that has a major influence on the national GDP. Model Regression analysis is a statistical method used to investigate and model the relationship between a dependent variable and independent variables. However, it is important to make sure that the underlying model assumptions are not violated. The main assumptions include; The sample must be selected randomly and represent the entire population The variables should be linearly independent The residuals should be normally distributed, have constant variance and in control One of the key outputs in a regression analysis is the regression equation and correlation coefficients. In a multivariate regression, various variables are included. The main purpose is to create the understanding of the connection between the dependent variable and the independent variables. The model parameters are estimated from the data using the method of least squares. A multivariate regression defines the effect of change in the value of independent variables on the value of the dependent variable. A multivariate regression model will be used to forecast the sales of LG grills since there is one dependent variable and four independent variables. The multivariate regression model for the data (appendix 1) is; Regression Statistics Multiple R 0.193955 R Square 0.037618 Adjusted R Square -0.11636 Standard Error 2118.683 Observations 30 ANOVA   df SS MS F Significance F Regression 4 4386566 1096642 0.244305 0.910388 Residual 25 1.12E+08 4488816 Total 29 1.17E+08         Coefficients Standard Error t Stat P-value Lower 95.0% Upper 95.0% Intercept 9525.208 32046.53 0.297231 0.768746 -56475.9 75526.27 Income 0.039206 0.572737 0.068454 0.945968 -1.14037 1.218781 Advertising 0.01453 0.352743 0.041191 0.967471 -0.71196 0.741017 GDP 1.47E-07 5.72E-07 0.257473 0.798921 -1E-06 1.32E-06 Price -0.15658 0.207316 -0.75529 0.457135 -0.58356 0.270393 Y = 9525.08 + 0.04X1 + 0.01X2 + 1.47E - 07X3 – 0.16X4 Where, Y = average grill sales per quarter X1 = average median household income X2 = average advertising expenses X3 = quarterly GDP X4 = average price of grills Coefficients of the X variables measure the effect of the variables on the sale of grills. Sensitivity Analysis Sensitivity is analysed using the elasticity. The most common elasticity is the price elasticity of demand, which is the measures the relationship between changes in the quantity demanded of a good and the changes in the price of the good. The price elasticity of demand for grills is inelastic at when the elasticity is greater than 1. This indicates that the demand for grills changes more proportionately than the percentage change in the price of the grills. The price elasticity of demand is also elastic when the elasticity is between 0 and 1. This indicates that the change in demand for grills from one quarter to another in larger than the percentage change in the price of the grills. It shows that changes in price has an effect on the quantity demanded for the grills. Implications to Business Policy Apart from the effect of the price elasticity of demand, as discussed above, the demand for grills can be influenced by several other factors including; income distribution, population, and environmental consciousness (such as energy rating). Other factors that may well affect the sale of LG grills include; product quality, functionality, and manufacturing prowess. The company should also focus on the vital elements that will help it to attract customers and maintain a high Brand Loyalty in customers. Advertising is also important, so the company should continue to spend on advertising. Besides, it is necessary for LG to continually improve on the manufacturing processes so as to enhance the product quality. References 1. Bloomberg Terminal 2. Rumsey, D, (2011), Statistics for Dummies, Kindle. 3. www.lgnewsroom.com/about-lg-electronics/ Appendix The Data y x1 x2 x3 Period Sales Income Advertising '1000' GDP Price Q1 11020 54707.66 2159 9.27E+08 13237 Q2 9939 54413.25 1071 9.99E+08 9148 Q3 11697 54116.76 4021 9.98E+08 7766 Q4 12231 53818.17 1323 1.16E+09 7105 Q1 12681 53517.46 4319 1.23E+09 7753 Q2 9300 53214.62 4132 1.35E+09 8817 Q3 10885 52909.63 4247 1.46E+09 11956 Q4 8018 52602.49 1547 1.6E+09 12879 Q1 8262 52293.17 1830 1.78E+09 11577 Q2 7225 51981.66 3810 2.11E+09 11371 Q3 11848 51667.94 3092 2.5E+09 8283 Q4 7496 51352 3681 2.97E+09 7835 Q1 10578 51033.82 3406 3.26E+09 12940 Q2 9401 50713.39 4251 3.47E+09 13445 Q3 12892 50390.69 1418 4.49E+09 12012 Q4 13783 50065.7 1196 5.3E+09 12496 Q1 10340 49738.41 4461 6.23E+09 7530 Q2 7391 49408.8 1886 7.27E+09 9860 Q3 11510 49076.86 2496 6.85E+09 13773 Q4 10346 48742.57 3639 6.43E+09 12029 Q1 13303 48405.91 3709 5.98E+09 9670 Q2 10375 48066.86 3978 6.19E+09 13594 Q3 8195 47725.41 3448 6.14E+09 11947 Q4 8411 47381.55 2066 7.24E+09 12809 Q1 12917 47035.24 4883 7.97E+09 10725 Q2 8828 46686.49 4550 8.36E+09 11497 Q3 12675 46335.26 3000 8.28E+09 12071 Q4 9485 45981.55 3159 8.57E+09 10412 Q1 10328 45625.33 1499 8.15E+09 11184 Q2 13977 45266.59 4384 8.21E+09 9893 Read More