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Introduction
The term regression analysis is considered to be the most important statistical tool which is used to identify the relationship between the given variables. The two variables which are used to find the bond between them are the dependent variable and the other one is the independent variables. The hypothesis testing is conducted for the purpose to find whether the results depicted contain valuable meaning or not. This test contains meaningful results from the data set given and which contains odd or randomly selected data. The hypothesis test is the easiest way to conduct the hull hypothesis test of the given problem.
Additionally, the test is performed with the help of observed data or realistic values taken on a random basis. However, alternative hypotheses are used to find the distribution of the given data. If the result is depicted as a significant value, the test is called to be significant and vice versa. There are two types of data which are been performed in order to identify the value of significant. The first data is hypothesis testing and the other one is the alternative hypothesis. If the value is depicted as a significant outcome, the result will be called a hypothesis and correct.
This report will help to understand the hypothesis testing with the help of regression analysis. The objective of conducting this test is to find out the relationship between the given data set. Furthermore, this report will highlight the areas of significance which will help to verify whether the given data set contains enough evidence in order to select for future use.
Background of study
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The statistical test is conducted to conclude whether the decision will further proceed or not. The objective of conducting this test is to identify whether the given data is to be selected or rejected. Additionally, this test is used to verify whether there is enough evidence to reject or accept the given data. It is mainly referred to as the science used for the collection of the data and for uncovering the trends and patterns[1]. It basically includes the designing, planning and the analysis of the meaningful interpretation used for the reporting of all the findings of the respective research. All the calculation is mainly used in the manufacturing for the examining of the taste and the preference which is circulating in the market.
Hypothesis testing
Hypothesis testing is mainly referred to as an act where all the statistics is being performed for a better analysis of the tests. This data set and is representing the dataset of the population. It is referred to as the evidence which is mainly concerned with the plausibility of the total hypothesis data given[2]. It is referred to as the theory here which is used for the production of the equality between the sections of the population parameter. The term and the measure which can be easily used for referring to the opposite of the theory is the alternative hypothesis theory. The means of the total population is mainly referred to as the point which is never equal to the number zero.
Regression analysis
Regression analysis is mainly referred to as the total set of the method which is used for total estimation of the relationship. The relationship mainly measures the dependency between the independent and the dependent variable present in the dataset[3]. The independent variable is mainly referred to as an input used for the total assumption. However, it can also be referred to as a driver which can be changed so that the total impact can be easily accessed on the varieties of the dependent variable.
Descriptive analysis
The descriptive analysis is the most important process in the statistical part in order to analyze the data given. However, it becomes important to conduct correct method which will help to get statistical analysis. The descriptive analysis can be of various types which will help to understand the statistical concept and to figure out the exact analysis. The descriptive analysis can be observed with the help of scatter plot which has been observed underneath. From the given dataset of the basketball players with certain descriptive analysis, it can be identified the relationship between the two variables[4]. The two variables which have been used in this case are shot numbers and the other one is the final margin.
To conduct the test on R studio software, it is important to load the correct data set which will help to analyze the ultimate outcome. First of all the data set has been converted into CSV files which will be further used to conduct the test. Furthermore, the data set was given a name as "basketball" in order to make the data set more comfortable to use. However, to read the correct CSV file, the codes which have been used in this case is been mentioned underneath
basket<-read.csv("basketball.csv")
To verify whether the data set are been correctly used in the software, a summary test has been performed for which the codes are
summary(basket)
Figure 1: summary of the data
(Source: self-created)
From the above test of summary, it can be analyzed that mean and median of the given data of the final margin is 0.14 and 1.00 respectively. Similarly, the mean and median of the data short number is 6.51 and 5.00 respectively.
Figure 2: scatter plot
(Source: self-created)
This result will help to analyze that the data are correct and can be used further in order to perform the upcoming tests. Additionally, the scatter plot has been depicted to find a descriptive analysis of the given data set[5]. To find the scatter plot, the data which has been used in this case is the final margin and short number. From the below-performed test on the scatter diagram, it can be analyzed that, the data set has a positive relationship among them.
However, there are other ways to find the descriptive analysis as well those are descriptive analysis of the individual variables and also a combination of the given variables.
The scatter plot is the scatter diagram which is sued to depict the coordinated of the given data set[6]. These points are helpful to find values of variables and their position is the horizontal axis and even in the vertical axis.
Figure3: bar plot of short number
(Source: self-created)
The bar plot is another statistical tool which is used to identify the range of the given data set. From the above bar graph of the data short number, there are certain data whose range was found to be high in comparison to other data. This can be identified with the help of the given bar graph. It is important in the case where quantitative analysis are been provided with descriptive analysis. Thus, to analyze the value a bar plot has been conducted by taking the variable of a short number.
Inferential analysis
The inferential analysis is conducted for the purpose where the data set are been obtained randomly. There is a certain test which has been performed in order to conduct the inferential analysis. It is the qualitative way to identify the performance of the dataset[7]. The objective of conducting the test is to conclude the final result from the given data set. The test which has been performed in the inferential analysis is regression analysis. The regression analysis is useful to identify the relationship between the given two variables. The two variables which are used to conduct the test are the dependent variable and the other one is the independent variable.
Figure 4: correlation and regression analysis
(Source: self-crested)
The regression analysis is conducted to identify the relationship between the given data set. There are two sets of variables which have been taken to conduct the result are dependent variable and the other one is the independent variables[8]. The data set of short number is taken as dependent variables, on the other hand, the short distance and point's types have been taken as independent variables. The codes which have been used in this case are been mentioned underneath
lm(formula = basket$SHOT_NUMBER~basket$SHOT_DIST+basket$PTS_TYPE,data = basket)
However, it is important to conduct certain codes which will help the software to read the CSV file. The codes which have been used to read the CSV file is been mentioned underneath basket<-read.csv("basketball.csv")
There were other tests of regression analysis was performed to identify the relationship between the given data set. These data are close defender distance and the other data is FGM and points. Furthermore, the codes which have been used in this case are mentioned underneath.
lm(formula = basket$CLOSE_DEF_DIST~basket$FGM+basket$PTS,data = basket)
The data set is quantitative in nature which depict that whether the data set are quantitative in nature or qualitative in nature. From the above test of the regression analysis, it can be identified that the data which have been used depict a positive relationship among themselves. The value of intercept is 4.115; the value of FGM is -5.991 and for the data points its 2.704. These results were analyzed with the help of the coefficient of the data and regression analysis.
The descriptive statistics are completely different from the inferential analysis. The descriptive analysis is used to identify the data set are correct or not. On the other hand, the inferential analysis is used to find meaningful conclusions. The hypothesis testing is useful to find where the data set has a positive significant value or not[9]. The objective of finding a significant value is to objectify whether the model is going to be selected or not. It is referred to as the evidence which is mainly concerned with the plausibility of the total hypothesis of the data given data set.
However, alternative hypotheses are used to find the distribution of the given data. If the result is depicted as a significant value, the test is called to be significant and vice versa. The null hypothesis is been used to verify whether the data sets are equal or not. The importance of using this test is that this allows the researcher to reject or use the given data set. The term and the objective to measure that the null hypothesis can be easily used for referring to the opposite of the theory is the alternative hypothesis theory[10]. There are certain assumption which becomes important to be considered beforehand is that if the data reflects a lower value then the data needs to be rejected. On the other hand, if the data set reflects the correct values then the data set is going to be selected in the relevant fields of statistics
Conclusion
This report can be concluded by; the regression analysis was helpful to conduct the test and to find the relationship of the given data set. However, there are certain factors which were considered like a null hypothesis which is used to find whether the data set is to be selected further or not. From the test performed above, it can only be suggested that the data will be helpful to select further as the results reflect a positive outcome. These tests were performed in the software called R studio which was helpful to find the accurate results from the tests performed.
Furthermore, the correlation has been used to find the degree of the data set. The objective of performing this test as it will help to find the degree of movement of the given data set. The inferential analysis is used to objectify the data are been selected randomly. The objective of conducting the test is to conclude the final result from the given data set. Additionally, in the descriptive analysis, the tests which have been performed are scattered plot and bar diagram. The data set which has been used is the final margin and short number in order to obtain the scatter plot.
In the performed tests, all the relevant examples are being taken into consideration for the further assumption related to the parameter of the population. The total methodology which is being presented is mainly focusing on the employment for the analyst. Thus, it can only be suggested that the data are positive in nature and have a strong relationship among them. This was obtained with the help of the regression analysis.
References
Austin, P. C., & Merlo, J. (2017). Intermediate and advanced topics in multilevel logistic regression analysis. Statistics in medicine, 36(20), 3257-3277. Retrieved on 10 February 2021 from: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.7336
Bafadal, I., Juharyanto, J., Nurabadi, A., & Gunawan, I. (2018, October). Principal Leadership and its Relationship with Student Learning Achievements: A Regression Analysis. In 3rd International Conference on Educational Management and Administration (CoEMA 2018) (pp. 156-158). Atlantis Press. Retrieved on 10 February 2021 from: https://download.atlantis-press.com/article/25903280.pdf
Carriere, M., Michel, B., & Oudot, S. (2018). Statistical analysis and parameter selection for mapper. The Journal of Machine Learning Research, 19(1), 478-516. Retrieved on 10 February 2021 from: https://www.jmlr.org/papers/volume19/17-291.pdf
Daoud, J. I. (2017, December). Multicollinearity and regression analysis. In Journal of Physics: Conference Series (Vol. 949, No. 1, p. 012009). IOP Publishing. Retrieved on 10 February 2021 from: https://iopscience.iop.org/article/10.1088/1742-6596/949/1/012009/pdf
Darlington, R. B., & Hayes, A. F. (2017). Regression analysis and linear models. New York, NY: Guilford. Retrieved on 10 February 2021 from: https://doc1.bibliothek.li/acd/FLMF052309.pdf
Gorbenko, I., Kuznetsov, A., Gorbenko, Y., Vdovenko, S., Tymchenko, V., & Lutsenko, M. (2019). Studies on statistical analysis and performance evaluation for some stream ciphers. International Journal of Computing, 18(1), 82-88. Retrieved on 10 February 2021 from: https://arxiv.org/pdf/1705.11055
Goss-Sampson, M. (2019). Statistical analysis in JASP: A guide for students. Retrieved on 10 February 2021 from: http://gala.gre.ac.uk/id/eprint/25585/7/25585%20GOSS-SAMPSON_Statistical_Analysis_In_JASP_A_Guide_For_Students_%28Pub%29_2019.pdf
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons. Retrieved on 10 February 2021 from: http://sutlib2.sut.ac.th/sut_contents/H133678.pdf
Nordhaus, W. D., & Moffat, A. (2017). A survey of global impacts of climate change: replication, survey methods, and a statistical analysis. Retrieved on 10 February 2021 from: https://www.nber.org/system/files/working_papers/w23646/w23646.pdf
Von Rosen, D. (2018). Bilinear regression analysis. Lecture notes in statistics, 220. Retrieved on 10 February 2021 from: http://ndl.ethernet.edu.et/bitstream/123456789/64352/1/409.pdf
[1] Daoud, J. I. (2017, December). Multicollinearity and regression analysis. In Journal of Physics: Conference Series (Vol. 949, No. 1, p. 012009). IOP Publishing. Retrieved on 10 February 2021 from: https://iopscience.iop.org/article/10.1088/1742-6596/949/1/012009/pdf
[2] Austin, P. C., & Merlo, J. (2017). Intermediate and advanced topics in multilevel logistic regression analysis. Statistics in medicine, 36(20), 3257-3277. Retrieved on 10 February 2021 from: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.7336
[3] Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons. Retrieved on 10 February 2021 from: http://sutlib2.sut.ac.th/sut_contents/H133678.pdf
[4] Bafadal, I., Juharyanto, J., Nurabadi, A., & Gunawan, I. (2018, October). Principal Leadership and its Relationship with Student Learning Achievements: A Regression Analysis. In 3rd International Conference on Educational Management and Administration (CoEMA 2018) (pp. 156-158). Atlantis Press. Retrieved on 10 February 2021 from: https://download.atlantis-press.com/article/25903280.pdf
[6] Von Rosen, D. (2018). Bilinear regression analysis. Lecture notes in statistics, 220. Retrieved on 10 February 2021 from: http://ndl.ethernet.edu.et/bitstream/123456789/64352/1/409.pdf
[7] Goss-Sampson, M. (2019). Statistical analysis in JASP: A guide for students. Retrieved on 10 February 2021 from: http://gala.gre.ac.uk/id/eprint/25585/7/25585%20GOSS-SAMPSON_Statistical_Analysis_In_JASP_A_Guide_For_Students_%28Pub%29_2019.pdf
[8] Nordhaus, W. D., & Moffat, A. (2017). A survey of global impacts of climate change: replication, survey methods, and a statistical analysis. Retrieved on 10 February 2021 from: https://www.nber.org/system/files/working_papers/w23646/w23646.pdf
[9] Carriere, M., Michel, B., & Oudot, S. (2018). Statistical analysis and parameter selection for mapper. The Journal of Machine Learning Research, 19(1), 478-516. Retrieved on 10 February 2021 from: https://www.jmlr.org/papers/volume19/17-291.pdf
[10] Gorbenko, I., Kuznetsov, A., Gorbenko, Y., Vdovenko, S., Tymchenko, V., & Lutsenko, M. (2019). Studies on statistical analysis and performance evaluation for some stream ciphers. International Journal of Computing, 18(1), 82-88. Retrieved on 10 February 2021 from: https://arxiv.org/pdf/1705.11055
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