4. There are numerous types of correlation coefficients, the most common of which is the Pearson Correlation Coefficient (PCC). 1. 96\)The common alternative to Karl Pearsons \(r\)is Spearmans \(\rho \). Q.
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The correlation between, say, two variables are negative when the direction of change of the variables is opposite i. 61)(22. The closer the value of 𝑟
is to −1 or 1, the stronger the
association, and the closer it is to 0, the weaker the association. The vertical distances between the actual value of variable Y (Y1, Y2 and Y3) and computed value of Y gives the residuals (error term).
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This coefficient provides a measure of linear association between ranks assigned to these units, not their values. In other words, whether the association between two ordered variables has a monotonic component. Since 8 appears in both the fourth and fifth positions in our ordered list, we will assign each
instance of 8 a rank equivalent to the average of their positions, or a rank of
4+52=92=4. Ans:Let, \(X\)represent the \(\%\) of students having free meals and\(y\)represent the \(\%\) of students scored CGPA above \(8.
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R=0. 614285…=0. How to calculate Spearmans rank correlation coefficient?Ans: The rank correlation coefficient is denoted by \(\rho \) or \({r_S}\) and can be calculated using the formula\(\rho = {r_S} = 1 \frac{{6\sum {d_i^2} }}{{n\left( {{n^2} 1} \right)}}\)Here,\(\rho =\) the strength of the rank correlation between variables\({d_i} = \) the difference between the \(x\) rank and the \(y\) rank for each pair of data\(\sum {d_i^2} = \) sum of the squared differences between \(x\) and \(y\) variable ranks\(n=\) sample sizeQ. (i) Commodity 1 and commodity 2(ii) Commodity 1 and profits(iii) Commodity 2 and profits(g) Using the table in (f) above, calculate the profit made from selling a single commodity of each. The sales revenue has been classified in different rows and the advertisement expense is shown in different columns.
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if as one variable increases, then the other variable also increases or if as one variable decreases, then the other variable also decreases. This value is added as many times as the number of such groups. Putting the
click to read values in order from best to worst gives us
ExcellentExcellentExcellentExcellentGoodGood,,,,,. . Here, 4 gets a rank of 1.
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We
will begin by assigning a rank to each of Read More Here pieces of data,
starting with the 𝑥-values. It is simple to understand and calculate. If we want to see the relationship between qualitative characteristics, the only formula we have is the rank correlation coefficient. -1 ≤ rk ≤ +1 Rank Correlation Coefficient ExampleCalculate the Rank Correlation Coefficient in each of the following cases:To calculate the rank correlation coefficient, first we will determine the value of D = R1 – R2 in each of the entries:Then the Spearman’s rank correlation coefficient is calculated using the formula as:rk = 1 – [6 ∑D2 / N3 – N] = 1- 6(0) = +1 Thus the value of rank correlation coefficient equal to +1 implies that there is complete agreement in the order of ranks and the ranks are in the same direction.
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Also, since “Poor” is in the fifth and sixth positions, we can assign each one a rank of
5+62=112=5. Round i thought about this answer to three decimal places. We also know that a rank of 6 should be used for 12, since 12 is in the sixth position in the list. .