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Q1: Define the following terms:(dont' use handwriting, please)

a. correlation coefficient(dont' use handwriting, please)

b. scatter plot(dont' use handwriting, please)

c. bivariate relationship(dont' use handwriting, please)

Q2: Provide an example where the outlier is more important to the research than the other observations?(dont' use handwriting, please)

Q3: Identify when to use Spearman’s rho (dont' use handwriting, please)

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ANSWER 1. -

(a). correlation coefficient -->

A relationship coefficient is a factual proportion of how much changes to the estimation of one variable foresee a change to the estimation of another. In decidedly corresponded factors, the esteem increments or declines couple. In adversely associated factors, the estimation of one increment as the estimation of alternate reductions.

Connection coefficients are communicated as qualities among +1 and - 1. A coefficient of +1 shows an ideal positive relationship: An adjustment in the estimation of one variable will anticipate an adjustment in a similar way in the second factor. A coefficient of - 1 demonstrates an ideal negative relationship: An adjustment in the estimation of one variable predicts an adjustment the other way in the second factor. Lesser degrees of relationship are communicated as non-zero decimals. A coefficient of zero demonstrates there is no discernable connection between changes of the factors

(b) scatter plot-->

A scatter plot is an arrangement of focuses plotted on even and vertical tomahawks.

Dissipate plots are imperative in measurements since they can demonstrate the degree of connection, assuming any, between the estimations of watched amounts or wonders (called factors). On the off chance that no connection exists between the factors, the focuses show up haphazardly scattered on the arranging plane. On the off chance that a huge relationship exists, the focuses think close to a straight line.

(c) Bivariate relationship --> Categorical data

• the goal of bivariate analysis: measure association or relationship
• crosstabulation = cross-classification =      contingency table
• intersecting frequency distributions
• rows, columns
• cells
• marginals - row and column totals
• r x c designation of a table - # rows x # columns
• computing row or column percentages.​

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