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(PDF) Essentials of Business Research Methods

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Added on  2021-05-30

(PDF) Essentials of Business Research Methods

   Added on 2021-05-30

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Question 1The objective is to ascertain if there is any significant difference in the average prices of residential properties in Coastal City 2 situated in State A and Coastal City 1 situated in State B.The relevant hypotheses are as stated below.H0(Null Hypothesis): μCity 2 = μCity 1H1(Alternative Hypothesis): μCity 2 ≠ μCity 1It is imperative to note that since the population standard deviation is unknown for bothdatasets, hence t would be used as the test statistics. Considering that there are twoindependent samples, hence two independent samples t test would be used. The test is twotailed considering the alternative hypothesis (Hillier, 2016). The relevant excel output isindicated below.The relevant p value as highlighted is 0.473. Considering an assumed level of significance of5%, it is apparent that the p values exceeds α. Therefore, available evidence is not sufficientto reject the null hypothesis and therefore alternative hypothesis would not be accepted(Flick, 2015). Hence, it may be concluded that there is no significant difference in theaverage prices of residential properties in the two cities.
(PDF) Essentials of Business Research Methods_1
Question 2The relevant scatter plot between house prices ($ 000’s) and internal area (m2) is indicatedbelow.It is apparent from the above scatter plot that there is a positive association between internalarea and price and the relationship does seem strong considering the overall linear trend inthe scatter plot. The relevant simple linear regression output is indicated below.
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ComputationsThe regression line equation is indicated below.House price ($ 000’s) = 205.32 + 1.92* Internal Area (m2)Coefficient of Determination or R2 = 0.5586Coefficient of Correlation = √0.5586 = 0.75InterpretationsThe slope coefficient is 1.92 which implies that an increase in internal area of the house by1m2 would lead to an average increase in the value of residential property by $ 1,920approximately, Further, the given slope coefficient is statistically significant even at 1%significance level, considering the corresponding p value is zero (Hair et. al., 2015).The intercept is 205.32 which implies that for a residential property with zero internal area,the price would be $ 205,320. Clearly, this is impractical since every residential propertywould have some area (Eriksson and Kovalainen, 2015).The correlation of coefficient implies that a strong positive association exists between theinternal area of the residential property and the price of property. This is in line with thescatter plot (Hillier, 2016).Further, the coefficient of determination is 0.5586 which implies that 55.86% of the changeswitnessed in the price of residential properties can be explained through correspondingchanges in internal area. This also is broadly in line with the observation in the scatter plot(Flick, 2015).Question 3The multiple regression model has been constructed using price ($000’s) as the dependentvariable and internal area, number of bedrooms and type (0 = unit & 1= house) as theindependent variables. The output obtained from excel is highlighted as shown below.
(PDF) Essentials of Business Research Methods_3

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