Correlation and Regression

 

These weekly exercises provide the opportunity for you to understand and apply statistical methods and analysis. Unless otherwise stated, use 5% (.05) as your alpha level (cutoff for statistical significance).
1. What information does a correlation coefficient convey?
2. State whether each of the following is an example of a positive correlation or a negative correlation.
1. Higher education level is associated with a larger annual income.
2. Increased testosterone is associated with increased aggression.
3. The smaller the class size, the more students believe they are receiving a quality education.
4. Rising prices of apples are associated with the sale of fewer apples.
3. Which is the predictor variable (X) and which is the criterion variable (Y) for each of the following examples?
1. A researcher tests whether the size of an audience can predict the number of mistakes a student makes during a classroom presentation.
2. A military officer tests whether the duration of an overseas tour can predict the morale among troops overseas.
3. A social psychologist tests whether the size of a toy in cereal boxes can predict preferences for that cereal.
Use SPSS and the data file found in syllabus resources (DATA540.SAV) to answer the following questions. Round your answers to the nearest dollar, percentage point, or whole number.
4. What is the regression equation that would best predict relationship happiness (HAPPY) from the Lifestyle (L) score?
a. HAPPY = L – .143
b. HAPPY = .23L – 4.5
c. HAPPY = .42L + .23
d. HAPPY = 4.47 – .018L
5. The Lifestyle score (L) measures the degree to which a participant desires a luxurious lifestyle. The Dependency score (D) measures the degree to which a participant expects others to provide financial support. Compute the correlation between these two variables. Which of the statements below best describes the relationship?
a. People who want a more frugal lifestyle tend to be more financially dependent.
b. People who want a more luxurious lifestyle tend to be more financially dependent.
c. People who want a more luxurious lifestyle tend to be less financially dependent.
d. There is no relationship between desired lifestyle and financial dependency.
6. What is the Pearson r correlation between participants’ ages and the age of their partners (AGE1, AGE2)?
a. .000
b. .413
c. .622
d. .822
7. Look at the correlation between Risk-Taking (R) and Relationship Happiness (HAPPY). Use the standard alpha level of 5%. How would you describe the relationship?
a. The relationship is non-significant.
b. There is a significant negative relationship.
c. There is a significant positive relationship.
d. The correlation is zero.
8. If you randomly chose someone from this sample, what is the chance that they described their relationship as either Happy or Very Happy?
a. 32%
b. 37%
c. 56%
d. 69%
Activity Outcomes
Examine the basic assumptions underlying statistical operations
Demonstrate the ability to analyze data

 

Sample Solution

Transient memory is the memory for a boost that goes on for a brief time (Carlson, 2001). In reasonable terms visual transient memory is frequently utilized for a relative reason when one can’t thoroughly search in two spots immediately however wish to look at least two prospects. Tuholski and partners allude to momentary memory similar to the attendant handling and stockpiling of data (Tuholski, Engle, and Baylis, 2001).

They additionally feature the way that mental capacity can frequently be antagonistically impacted by working memory limit. It means quite a bit to be sure about the typical limit of momentary memory as, without a legitimate comprehension of the flawless cerebrum’s working it is challenging to evaluate whether an individual has a shortage in capacity (Parkin, 1996).

 

This survey frames George Miller’s verifiable perspective on transient memory limit and how it tends to be impacted, prior to bringing the examination state-of-the-art and outlining a determination of approaches to estimating momentary memory limit. The verifiable perspective on momentary memory limit

 

Length of outright judgment

The range of outright judgment is characterized as the breaking point to the precision with which one can distinguish the greatness of a unidimensional boost variable (Miller, 1956), with this cutoff or length generally being around 7 + 2. Mill operator refers to Hayes memory length try as proof for his restricting range. In this members needed to review data read resoundingly to them and results obviously showed that there was a typical maximum restriction of 9 when double things were utilized.

This was regardless of the consistent data speculation, which has proposed that the range ought to be long if each introduced thing contained little data (Miller, 1956). The end from Hayes and Pollack’s tests (see figure 1) was that how much data sent expansions in a straight design alongside how much data per unit input (Miller, 1956). Figure 1. Estimations of memory for data wellsprings of various sorts and bit remainders, contrasted with anticipated results for steady data. Results from Hayes (left) and Pollack (right) refered to by (Miller, 1956)

 

Pieces and lumps

Mill operator alludes to a ‘digit’ of data as need might have arisen ‘to settle on a choice between two similarly probable other options’. In this manner a basic either or choice requires the slightest bit of data; with more expected for additional complicated choices, along a twofold pathway (Miller, 1956). Decimal digits are worth 3.3 pieces each, implying that a 7-digit telephone number (what is handily recollected) would include 23 pieces of data. Anyway an evident inconsistency to this is the way that, assuming an English word is worth around 10 pieces and just 23 pieces could be recollected then just 2-3 words could be recalled at any one time, clearly mistaken. The restricting range can all the more likely be figured out concerning the absorption of pieces into lumps.

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