Please respond to the 3 discussion responses below. The reply must summarize th

Do you need this or any other assignment done for you from scratch?

We have qualified writers to help you.

We assure you a quality paper that is 100% free from plagiarism and AI.

You can choose either format of your choice ( Apa, Mla, Havard, Chicago, or any other)

NB: We do not resell your papers. Upon ordering, we do an original paper exclusively for you.

NB: All your data is kept safe from the public.

Click Here To Order Now!

Please respond to the 3 discussion responses below. The reply must summarize th

Please respond to the 3 discussion responses below. The reply must summarize the student’s findings and indicate areas of agreement, disagreement, and improvement. It must be supported with scholarly citations in the latest APA format and corresponding list of references to each response. The minimum word count for Integrating Faith and Learning discussion reply is 250 words.
1. Laura
D6.8.1 Why would we graph scatterplots and regression lines?
D6.8.1. The overall purpose of the scatterplot is to let you know if there is indeed a correlation between the two different variables and the impact that each variable may have one the other variable (Morgan, 2020). It is important for the end user to know the correlation of both variables. The regression line can be used to predict the value of y for a given value of x.
D6.8.2 In Output 8.2, (a) What do the correlation coefficients tell us? (b) What is r2 for the Pearson correlation? What does it mean? (c) Compare the Pearson and Spearman correlations on both correlation size and significance level; (d) When should you use which type in this case?
D6.8.2 (A) The correlation coefficients tell us the specific measurement that defines the strength of the linear relationship between the two variables that are being analyzed (Fina, 2023). There is a significant correlation between math achievement and mother’s education for both the Spearman and Pearson statistic. In each case the p value is significant below the .01 level
D6.8.2(B) The r2 for the Pearson correlation shows how the variables are related in a straight line and this means that the higher the mother’s education the higher she scored on the math achievement test. r^2 = 0.114. That tells us that apparently 11% of variance is shared, or if you know mother’s education you can accurately predict math achievement scores in about 11% of the cases.
D6.8.2 (C ) The Pearson and Spearman correlations on both correlation size and significance level overall tells us that the students who have mothers that have a higher education level were also able to achieve higher math achievement test scores. The correlation is very similar in this case. Spearman is .315 and the Pearson is .338. Both are significant at the .01 level.
D6.8.2(D) Spearman is used if one of the variables is not normally distributed or ordinal. Pearson is used when both variables are normally distributed or interval.
D6.8.5 In Output 8.5, what do the standardized regression weights or coefficients tell you about the ability of the predictors to predict the dependent variable?
D6.8.5 Standardized regression weights or coefficients tell you about the ability of the predictors to predict the dependent variable whether there is a positive or negative correlation between each independent variable and the dependent variable. If there is a positive coefficient indicates that as the value of the independent variable increases and the mean of the dependent variable can increase as well.
References:
Fina, M., Lauff, C., Faes, M. G., Valdebenito, M. A., Wagner, W., & Freitag, S. (2023). Bounding imprecise failure probabilities in structural mechanics based on maximum standard deviation. Structural Safety, 101, 102293.
Morgan, G. A., Barrett, K. C., Leech, N. L., & Gloeckner, G. W. (2020). IBM SPSS for Introductory Statistics: Use and Interpretation: Use and Interpretation. Routledge.
2. Robert
D6.8.1 Scatterplots and regression lines
According to Morgan (2020), scatterplots provide a visual picture of the correlation. The Plot allows you to see any bivariate outlier implying that there is more of a curve to the data than a straight line. This would show the strength of the relationship between the two variables. The linear regression line helps to see the correlation between variables and checks if the linearity assumption is met or not. This helps determine the use of a Pearson or Spearman’s Correlation (Morgan, Barrett, Leech, & Gloeckner, 2020).
D6.8.2 Pearson and Spearman correlations
The Pearson Correlation coefficient is 0.34 and the significance is .003. The p-value is .05, since the sig is smaller than the p-value, the null hypothesis should be rejected. However, in this case, mother’s education is skewed according to Morgan et. al (2020), so a Spearman’s Ryo should be computed. The Spearman’s rho is 0.315 with a sig of .006, since the p-value is .05 we can conclude that there is a positive correlation between Mother’s education and math achievement scores. With the r2 score we can predict that there is an 11% variance in the predictions between the two variables.
The r2 for the Pearson correlation is (0.338)2 or 0.114. If the R2 is 0 then there is no correlation. If the R2 is between 0-1 then there is some correlation and if the r2 is 1 then the correlation is perfect. This is the strength of the variance that will allow us to use predictive analytics.
Pearson correlation coefficient is 0.338; and the Sig is .003, while Spearman’s rho is .315, with a sig of .006; The decision between which one to use comes down to the skewness of the variables, as Pearson assumes a normal distribution of the data. Morgan et al (2020) states that the data in mothers’ education data is skewed, it is inappropriate to use a Pearson correlation coefficient.
D6.8.5 standardized regression weights or coefficients
According to Morgan et al (2020), the standardized coefficients allow the researcher to compare the amount that each variable contributes to predicting man’s achievements when all variables are used as predictors. Standardizing the coefficients on the same scale allows a researcher to compare them accurately.
References
Morgan, G. A., Barrett, K. C., Leech, N. L., & Gloeckner, G. W. (2020). IBM SPSS for Introductory Statistics Use and Interpretation. New York, NY: Routledge.
3. Yanitza
A6.D6.8.1 Why would we graph scatterplots and regression lines?
The scatterplots are for two variables, showing the score of one variable associated with another. The scatterplot also allows us to see if it is far from the regression line. This may indicate if a curved line is better than a straight line. If the correlation is highly positive, it will be close to a straight line. This provides a visual illustration of the correlation. This allows us to see if a Pearson correlation is not the best choice and consider the Kendall Tau-b or Spearman correlation.
A6.D6.8.2 In Output 8.2, (a) What do the correlation coefficients tell us?
The Pearson Correlation tells us that the significance level is .003, and the number of participants is 75. for both variables. It also describes the degrees of freedom marked in parentheses. The N-2 for correlation and the r for Pearson correlation with the r italicized as a statistical symbol. The orp value follows as asp=.003
A6.D6.8.2(b) What is r2 for the Pearson correlation? What does it mean?
The Pearson correlation r2 indicates the proportion of the variance that can be predicted. The Pearson correlation is based on ranking scores and does not necessarily use the raw score. This correlation checks the assumption of a linear regression and adjusts for the number of predictors.
A6.D6.8.2(c) Compare the Pearson and Spearman correlations on both correlation size and significance level.
The Pearson correlation is used when both variables are normally distributed, or assumptions are marked violated. The Spearman correlation can be calculated if one or both variables are ordinal and not normally distributed. The Spearman correlation is based on ranking scores and is used when the scores are ordinal.
When should you use which type in this case?
The Pearson correlation is used when two variables are standard, and the Spearman correlation is used when one or both variables are ordinal but not normally distributed.
A6.D6.8.5 In Output 8.5, what do the standardized regression weights or coefficients tell you about the ability of the predictors to predict the dependent variable?
The standardized regression or coefficient contributes to the prediction by comparing the amount of each variable when these are used as predictors in the same scale. The standardized coefficient is better for predicting the dependent variable and its significance for the investigation. It is essential to note that all the variables are considered when the values are computed. Each of the variables is significant to one another. However, if one predictor is removed from the equation, it affects the significance level. There is a problem if there is a high correlation among the predictive variables. However, this can happen if two or more predictors measure similar information.
Reference
Morgan, G. A. (2020). IBM SPSS for introductory statistics: Use and interpretation. Routledge.

Do you need this or any other assignment done for you from scratch?

We have qualified writers to help you.

We assure you a quality paper that is 100% free from plagiarism and AI.

You can choose either format of your choice ( Apa, Mla, Havard, Chicago, or any other)

NB: We do not resell your papers. Upon ordering, we do an original paper exclusively for you.

NB: All your data is kept safe from the public.

Click Here To Order Now!