DNP 830 Topic 6 Assignment Working With Inferential Statistics
DNP 830 Topic 6 Assignment Working With Inferential Statistics
DNP 830 Topic 6 Assignment Working With Inferential Statistics
DNP 830 Topic 6 Assignment Working With Inferential Statistics
Working With Inferential Statistics
Inferential statistics is a branch of statistics that applies various analytical tools in drawing conclusions about the population by examining random samples. Again, they give finer details on the sampled data, including the association between variables in a dataset (Amrhein et al., 2019). The main goal of inferential statistics is to make a generalization about a population. Some of the tools important in inferential statistics include analysis of variance (ANOVA), t-test, and others. In this DNP project, these two inferential statistics would be of great significance in analyzing the sampled population.
Mean, Standard Deviation, and Range
The mean of the discharges in 2019 is 720,811.89 showing that the number of discharges has been increasing since 2017. The mean also gives a general impression that discharges are significant in the healthcare system. Again, the standard deviation of the discharges from the mean is 823,479.26 The SD does not deviate much from the population, implying that the discharges recorded from all the states do not vary by a wider margin (Mishra et al., 2019). Finally, the range of the discharge summaries recorded in 2019 is 3,770,020.
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ANOVA
The discharge data collected in 2019 is significant as it shows one of the years that each state recorded a significant number of discharges. The data in 2019 compared with the data in other years included in the study because it shows a high trend which is seen decreasing in 2020. This high discharge recorded this year relays more information about the health system in the US by 2019. The variance between groups and within groups of the other years with the data collected in 2019 is significant, as shown in the appendix. The western and mid-western states have a small variance, while southern and northeastern states have a fairly large variance. However, the variance recorded in all these groups is significant because the p<0,000, which is significant for all the states (Amrhein et al., 2019).
T-Test
The paired sampled t-test shows a bivariate Pearson correlation coefficient that gives hypothesized results. The paired samples between 2012 and 2019 have a t= 0.493. This implies that there are significantly more discharges in 2019 as compared to 2012. The paired sampled t-test assumes that the mean of the patients discharged in 2012 and 2019 are not the same (Stapor, 2020). This shows that the mean of patients discharged in 2012 is low while the mean of those discharged in 2019 is high. This shows an improvement in the number of patients discharged in 2019 than 2012. The test further shows that the discharge summaries recorded in 2012 are low, while there has been a steady increase in the discharge summaries over the years.
Conclusion
In this paper, the author has provided ANOVA and T-test output as some of the analytical tools used in the generalization of the data in 2019. These tools will be significant in my DNP project as they form the insights that will be significant in analyzing my data. I will also apply inferential statistics to the prospectus for the DPI project.
References
Amrhein, V., Trafimow, D., & Greenland, S. (2019). Inferential statistics as descriptive statistics: There is no replication crisis if we don’t expect replication. The American Statistician, 73(sup1), 262-270. https://doi.org/10.1080/00031305.2018.1543137
Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of cardiac anaesthesia, 22(1), 67. https://doi.org/10.4103%2Faca.ACA15718
Stapor, K. (2020). Descriptive and inferential statistics. In Introduction to Probabilistic and Statistical Methods with Examples in R (pp. 63-131). Springer, Cham. DOI: 10.1007/978-3-030-45799-02
Appendices
Statistics
2019
N
Valid
33
Missing
3
Mean
720811.8485
Median
473313.0000
Mode
57180.00a
Std. Deviation
823479.25580
Range
3770020.00
Minimum
57180.00
Maximum
3827200.00
a. Multiple modes exist. The smallest value is shown
ANOVA
Sum of Squares
df
Mean Square
F
Sig.
2012
Between Groups
10622628681292.960
25
424905147251.718
.
.
Within Groups
.000
0
.
Total
10622628681292.960
25
2013
Between Groups
10365010799004.518
26
398654261500.174
.
.
Within Groups
.000
0
.
Total
10365010799004.518
26
2014
Between Groups
10645313957132.678
27
394270887301.210
.
.
Within Groups
.000
0
.
Total
10645313957132.678
27
2015
Between Groups
11219616544191.310
28
400700590863.975
.
.
Within Groups
.000
0
.
Total
11219616544191.310
28
2016
Between Groups
11570788618594.668
29
398992710986.023
.
.
Within Groups
.000
0
.
Total
11570788618594.668
29
2017
Between Groups
11699386760499.500
31
377399572919.339
.
.
Within Groups
.000
0
.
Total
11699386760499.500
31
2018
Between Groups
21562390146133.336
32
673824692066.667
.
.
Within Groups
.000
0
.
Total
21562390146133.336
32
2020
Between Groups
15100282452033.887
25
604011298081.356
.
.
Within Groups
.000
0
.
Total
15100282452033.887
25
Paired Samples Statistics
Mean
N
Std. Deviation
Std. Error Mean
Pair 1
2012
686773.0385
26
651847.48772
127837.80999
2019
679626.0000
26
654549.84684
128367.78622
Paired Samples Correlations
N
Correlation
Significance
One-Sided p
Two-Sided p
Pair 1
2012 & 2019
26
.994
<.001
<.001
Paired Samples Test
Paired Differences
t
df
Significance
Mean
Std. Deviation
Std. Error Mean
95% Confidence Interval of the Difference
One-Sided p
Two-Sided p
Lower
Upper
Pair 1
2012 – 2019
7147.03846
73894.48405
14491.90062
-22699.58957
36993.66649
.493
25
.313
.626
Paired Samples Effect Sizes
Standardizera
Point Estimate
95% Confidence Interval
Lower
Upper
Pair 1
2012 – 2019
Cohen’s d
73894.48405
.097
-.290
.481
Hedges’ correction
76207.72538
.094
-.281
.466
a. The denominator used in estimating the effect sizes.
Cohen’s d uses the sample standard deviation of the mean difference.
Hedges’ correction uses the sample standard deviation of the mean difference, plus a correction factor.
General Requirements:
This course helps you develop a basic understanding of statistics. Two distinct types of statistics are addressed: descriptive and inferential. In this assignment, you will have the opportunity to use the SPSS program. SPSS makes it easy to analyze data using specific tests. This assignment will give you practice with t-tests and ANOVA. Be sure to review the videos before undertaking this practice.
Use the following information to ensure successful completion of the assignment:
For assistance with accessing SPSS, refer to the resources “How to Use SPSS From the GCU Server” and “How to Install SPSS on Your Computer.”
Before beginning this assignment, be sure to view the tutorial videos provided as Topic Materials: (1) SPSS for Beginners 6a: One-Sample T-Tests and Confidence Intervals; (2) SPSS for Beginners 6c: Independent-Samples T-Tests and Confidence Intervals; (3) Oneway ANOVA – SPSS (Part 1); (4) ANOVA 1: Calculating SST (Total Sum of Squares); and (5) Introduction to Statistics: Inferential Statistics.
Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center.
You are not required to submit this assignment to LopesWrite.
Directions:
Open SPSS and complete the following:
SPSS Output
Open SPSS and obtain an output (as in the tutorial videos) with the following results highlighted.
Have children exposed to movies created before 1980 caused more injuries than children exposed to movies after 1980?
Which group has caused more injuries: children exposed to movies created between 1937-1960, children exposed to movies created between 1961-1989, or children exposed to movies created between 1990-1999?
Determine the statistics using a one-tailed t-test (for question 1) and ANOVA (for question 2). Be sure to describe how you ensured that the assumptions for each test were met prior to doing the one-tailed t-test and ANOVA. Justify your choice with references.
Data Set:
“Data Set: Violence, Children, and Movies” is provided as a Topic Material.
Summary:
Write a 250-500 word summary of your results and how this statistical analysis may be applied to your prospectus. Use the “Working With Inferential Statistics Template” to present your data and embed the table in your paper. Include your SPSS output as an appendix in the paper.
Click here to ORDER an A++ paper from our Verified MASTERS and DOCTORATE WRITERS: DNP 830 Topic 6 Assignment Working With Inferential Statistics
ourse Code DNP-830 Class Code DNP-830-O501 Criteria Content Percentage 70.0% Summary of Results 20.0% Statistical Analysis: t-Test 15.0% Statistical Analysis: ANOVA 15.0% Appendix: Statistical Outputs 20.0% Organization and Effectiveness 20.0% Thesis Development and Purpose 7.0% Argument Logic and Construction 8.0% Mechanics of Writing (includes spelling, punctuation, grammar, language use) 5.0% Format 10.0% Paper Format (use of appropriate style for the major and assignment) 5.0% Documentation of Sources (citations, footnotes, references, bibliography, etc., as appropriate to assignment and style) 5.0% Total Weightage 100% Assignment Title Working with Inferential Statistics Unsatisfactory (0.00%) A summary of the results is not included. The t-Test statistical analysis is not included. The ANOVA statistical analysis is not included. Outputs from the statistical analysis are not included. Paper lacks any discernible overall purpose or organizing claim. Statement of purpose is not justified by the conclusion. The conclusion does not support the claim made. Argument is incoherent and uses noncredible sources. Surface errors are pervasive enough that they impede communication of meaning. Inappropriate word choice or sentence construction is used. Template is not used appropriately or documentation format is rarely followed correctly. Sources are not documented. Total Points 100.0 Less Than Satisfactory (74.00%) A summary of the results is incomplete or incorrect The t-Test statistical analysis is incomplete or incorrect. The ANOVA statistical analysis is incomplete or incorrect. Outputs from the statistical analysis are incomplete or incorrect. Thesis is insufficiently developed or vague. Purpose is not clear. Sufficient justification of claims is lacking. Argument lacks consistent unity. There are obvious flaws in the logic. Some sources have questionable credibility. Frequent and repetitive mechanical errors distract the reader. Inconsistencies in language choice (register) or word choice are present. Sentence structure is correct but not varied. Appropriate template is used, but some elements are missing or mistaken. A lack of control with formatting is apparent. Documentation of sources is inconsistent or incorrect, as appropriate to assignment and style, with numerous formatting errors. Satisfactory (79.00%) A summary of the results is included but lacks detail. The t-Test statistical is mostly incomplete or incorrect. The ANOVA statistical analysis is mostly incomplete or incorrect Outputs from the statistical analysis are mostly incomplete or incorrect. Thesis is apparent and appropriate to purpose. Argument is orderly, but may have a few inconsistencies. The argument presents minimal justification of claims. Argument logically, but not thoroughly, supports the purpose. Sources used are credible. Introduction and conclusion bracket the thesis. Some mechanical errors or typos are present, but they are not overly distracting to the reader. Correct and varied sentence structure and audience-appropriate language are employed. Appropriate template is used. Formatting is correct, although some minor errors may be present. Sources are documented, as appropriate to assignment and style, although some formatting errors may be present. Good (87.00%) A summary of the results is complete and includes supporting detail. The t-Test statistical analysis is mostly included or correct. The ANOVA statistical analysis is mostly included or correct Outputs from the statistical analysis are mostly included or correct. Thesis is clear and forecasts the development of the paper. Thesis is descriptive and reflective of the arguments and appropriate to the purpose. Argument shows logical progressions. Techniques of argumentation are evident. There is a smooth progression of claims from introduction to conclusion. Most sources are authoritative. Prose is largely free of mechanical errors, although a few may be present. The writer uses a variety of effective sentence structures and figures of speech. Appropriate template is fully used. There are virtually no errors in formatting style. Sources are documented, as appropriate to assignment and style, and format is mostly correct. Excellent (100.00%) A summary of the results is extremely thorough, with substantial supporting detail. The t-Test statistical analysis for is all complete and correct. The ANOVA statistical analysis is all complete and correct. Outputs from the statistical analysis are mostly included or correct. Thesis is comprehensive and contains the essence of the paper. Thesis statement makes the purpose of the paper clear. Clear and convincing argument that presents a persuasive claim in a distinctive and compelling manner. All sources are authoritative. Comments Writer is clearly in command of standard, written, academic English. All format elements are correct. Sources are completely and correctly documented, as appropriate to assignment and style, and format is free of error. Points Earned State 2010 2011 2012 2013 2014 AK AR 412,172 409,984 406,274 391,805 393,002 AZ 790,492 801,982 774,289 747,182 748,658 CA 3,970,921 3,933,239 CO 478,025 479,950 473,461 464,574 467,952 137,406 134,470 DC DE FL 2,640,092 2,656,249 2,670,520 2,673,488 2,741,984 GA 1,087,259 1,073,083 1,061,815 1,046,315 1,044,001 HI 135,533 134,824 120,426 118,982 118,120 IA 341,255 337,570 328,303 317,843 313,874 KS 322,687 316,425 315,718 306,833 306,798 KY 638,292 634,750 618,465 594,723 588,450 MA 846,093 849,997 819,305 794,745 790,338 MD 747,442 721,317 695,207 667,711 645,960 ME 155,674 155,493 150,945 145,255 145,686 MI 1,272,498 1,269,145 1,249,805 1,223,294 1,224,448 MN 609,467 597,645 583,342 577,381 586,926 347,690 378,167 1,099,789 1 091,956 1,084,667 MS 375,672 389,828 NC 1,129,367 1,111,961 NE 218,935 212,122 205,631 204,733 201,260 NJ 1,093,504 1,069,663 1,043,274 1,013,363 995,510 NM 209,073 206,720 198,884 201,233 196,150 NV 302,160 295,081 296,502 298,667 302,758 NY 2,612,610 2,578,680 2,529,422 2,419,318 2,367,188 OR 373,981 372,203 358,486 357,043 365,188 RI 139,735 137,504 133,958 127,346 131,368 SC 546,917 537,407 533,767 520,769 519,303 SD 103,668 104,101 105,429 105,227 105,429 UT 274,576 280,830 281,605 279,393 281,302 VT 52,965 52,214 52,180 51,211 49,564 WA 651,783 648,079 640,892 631,044 633,390 WI 631,418 628,134 626,629 611,758 602,982 WV 291,237 293,325 286,914 274,937 264,539 2015 2016 2017 2018 61,871 63,551 64,023 62,398 393,310 399,327 401,032 401,831 739,795 735,890 717,302 755,988 2019 771,497 3,819,392 478,382 480,573 483,166 481,359 135,240 134,859 131,380 127,483 112,117 112,422 112,897 2,817,621 2,837,863 2,847,000 2,855,604 1,065,110 1,070,471 1,089,399 1,101,923 121,057 118,398 319,434 312,732 340,611 330,988 314,563 315,216 320,692 320,411 600,662 600,851 603,131 597,633 796,716 803,070 811,627 807,125 628,251 622,815 613,079 598,751 144,922 144,421 145,288 141,375 1,235,001 1,240,053 1,234,065 1,212,995 586,978 586,624 603,642 606,811 329,367 589,019 582,026 587,473 380,906 378,494 389,006 382,288 1,101,318 1,110,146 1,117,059 1,123,093 202,601 202,658 207,638 205,706 985,912 979,099 952,312 941,250 197,725 200,458 201,474 201,308 367,355 367,096 367,657 322,239 337,305 354,480 2,346,869 2,347,084 2,362,414 375,160 374,965 375,941 373,127 132,789 134,405 135,298 131,443 526,562 530,183 533,330 528,910 106,091 106,150 105,924 105,475 291,025 296,903 291,934 52,755 53,652 53,630 53,560 647,555 649,624 654,444 643,855 605,479 602,279 602,090 584,533 262,113 255,066 258,609 259,625 204,530 584,456
Name: Assignment Rubric
Grid View
List View
Excellent
Good
Fair
Poor
Summarize your interpretation of the frequency data provided in the output for respondent’s age, highest school grade completed, and family income from prior month.
32 (32%) – 35 (35%)
The response accurately and clearly explains, in detail, a summary of the frequency distributions for the variables presented.
The response accurately and clearly explains, in detail, the number of times the value occurs in the data.
The response accurately and clearly explains, in detail, the appearance of the data, the range of data values, and an explanation of extreme values in describing intervals that sufficiently provides an analysis that fully supports the categorization of each variable value.
The response includes relevant, specific, and appropriate examples that fully support the explanations provided for each of the areas described.
28 (28%) – 31 (31%)
The response accurately summarizes the frequency distributions for the variables presented.
The response accurately explains the number of times the value occurs in the data.
The response accurately explains the appearance of the data, the range of data values, and explains extreme values in describing intervals that provides an analysis which supports the categorization of each variable value.
The response includes relevant, specific, and accurate examples that support the explanations provided for each of the areas described.
25 (25%) – 27 (27%)
The response inaccurately or vaguely summarizes the frequency distributions for the variables presented.
The response inaccurately or vaguely explains the number of times the value occurs in the data.
The response inaccurately or vaguely explains the appearance of the data, the range of data values, and inaccurately or vaguely explains extreme values.
An analysis that may support the categorization of each variable value is inaccurate or vague.
The response includes inaccurate and irrelevant examples that may support the explanations provided for each of the areas described.
0 (0%) – 24 (24%)
The response inaccurately and vaguely summarizes the frequency distributions for the variables presented, or it is missing.
The response inaccurately and vaguely explains the number of times the value occurs in the data, or it is missing.
The response inaccurately and vaguely explains the appearance of the data, the range of data values, and an explanation of extreme values, or it is missing.
An analysis that does not support the categorization of each variable values is provided, or it is missing.
The response includes inaccurate and vague examples that do not support the explanations provided for each of the areas described, or it is missing.
