STATISTICAL ANALYSIS

Trends and Consumerism in Health Care

Trends and Consumerism in Health Care

Descriptive Statistics

In this part, examples related to the sugar level before and after the meals are mentioned:

Sugar Level Before Meal (X)

(X - µ)

(Xn - µ)^2

6.5

0.6

0.4

7.1

1.2

1.5

5.1

-0.8

0.6

5.7

-0.2

0.0

5.2

-0.7

0.4

5.4

-0.5

0.2

6.1

0.2

0.1

6.2

0.3

0.1

7.1

1.2

1.5

4.2

-1.7

2.8

58.6

0.0

7.7

Mean

µ = (6.5+7.1+5.1+5.7+5.2+5.4+6.1+6.2+7.1+4.2)/10

µ = 58.6/10 = 5.86

Variance

s^2 = ? (Xn - µ)^2/N

s^2 = (0.4+1.5+0.6+0.0+0.4+0.2+0.1+0.1+1.5+2.8)/10

s^2 = 7.7/10 = 0.77

Standard Deviation

s = v s^2

s = v 0.77 = 0.88

Sugar Level After Meal (X)

(X - µ)

(Xn - µ)^2

7.1

-1.0

1.0

8.4

2.5

6.5

9.1

3.2

10.5

8.2

2.3

5.5

8.7

2.8

8.1

7.5

1.6

2.7

7.8

1.9

3.8

9.0

3.1

9.9

7.5

1.6

2.7

7.5

1.6

2.7

80.8

20.0

53.1

Mean

µ = (7.1+8.4+9.1+8.2+8.7+7.5+7.8+9.0+7.5+7.5)/10

µ = 80.8/10 = 8.08

Variance

s^2 = ? (Xn - µ)^2/N

s^2 = (1.0+6.5+10.5+5.5+8.1+2.7+3.8+9.9+2.7+2.7)/10

s^2 = 53.1/10 = 5.31

Standard Deviation

s = v s^2

s = v 5.31 = 2.31

Thus, the above analysis of sugar level before and after the meals indicates that there is too much variation and deviation in the sugar level before meals in comparison sugar level after meals.

Inferential Statistics

Inductive statistics includes procedures used in the analysis and interpretation of data to reach conclusions or inferences about large populations based on sample data associated with a margin of uncertainty. Inferential statistics allow us to use the samples to make general viewpoints about the population. Thus, it is necessary that appropriate sampling procedure is used. The methods of inferential statistics include estimation of parameter and testing of hypotheses.

Software Programs used in Healthcare Statistical Analysis

The software programs which are used in healthcare include Microsoft Excel, Statistical package for Social Sciences, Minitab etc. These software programs help the researchers in analyzing the given phenomena with appropriate statistical analysis. The statistical tests which can be applied in healthcare through these software programs include analysis of variance, correlation regression analysis and Chi-square etc.

References

Koch, G. (2007) Basic Allied Health Statistics and Analysis. Cengage Learning, 54-91. Retrieved from: https://books.google.com.pk/books?id=fwXLMm0uKaoC&pg=PA14&dq=descriptive+statistics+and+inferential+statistics&hl=en&sa=X&ei=FujxUIyPMNC10QX4zICACQ&redir_esc=y#v=onepage&q=descriptive%20statistics%20and%20inferential%20statistics&f=false

Spatz, C. (2010) Basic Statistics: Tales of Distributions. Cengage Learning, 16-87. Retrieved from: ...

ANALYSIS OF PUBLISHED ROUTINE DATA FROM THE OFFICE FOR NATIONAL STATISTICS NEIGHBOURHOOD STATISTICS OR HEALTH AND CARE WEB PAGES AND/OR OTHER APPROPRIATE DATA TO BE USED TO COMPARE AN AREA RELEVANT TO THE STUDENT, FOR EXAMPLE THEIR WARD OF WORK OR RESIDE

Analysis of published routine data from the Office for National Statistics Neighbourhood Statistics or Health and Care web pages and/or other appropriate data to be used to compare an area relevant to the student, for example their ward of work or reside



Analysis of published routine data from the Office for National Statistics Neighbourhood Statistics or Health and Care web pages and/or other appropriate data to be used to compare an area relevant to the student, for example their ward of work or ...

STATISTICS

Descriptive and Inferential Statistics

Descriptive and Inferential Statistics

Statistics is divided into two main branches namely descriptive statistics and inferential statistics.

Descriptive Statistics

Descriptive Statistics is used to organize and summarize sets of observations quantitatively. The summary can be done using tables, graphs or numerical values. The data sets containing observations of more than one variable can study the relationship or association between them. Descriptive Statistics is the counting, sorting and classifying data obtained by observation. Tables are constructed and plotted graphs, allowing simplify complex data involved in the distribution. Also calculated statistical parameters that characterize the distribution. No use is made ??of Probability Theory and only limited to directly make deductions from the data and parameters obtained (Kazmier et al, 2003).

Measures

One of the objectives of descriptive statistics is to summarize all the information gathered in a few numerical values ??to draw inferences from that information. Within the set of numerical values ??that summarize all the information they are of different types and contribute different characteristics (Kirk, 2008).

Centralization measures: mean, mode, median, barracks, deciles and percentiles

Measures of dispersion: variance, standard deviation, range, interquartile range

Form measures: Pearson's coefficient of variation, Fisher

Relationship between variables: linear correlation coefficient, regression line

Descriptive statistics quantitatively describes the main features of the data collection. Descriptive statistics are different from the Statistical inference (or inductive statistics), that the descriptive statistics to summarize the data set, and not to use the data to find that the population data are believed to represent.

Statistical Inference

Statistical Inference is used to infer something about a population based on data obtained from a sample. Statistical data are arithmetic performed on values ??obtained in a portion of the population, selected according to strict criteria. Statistical inference is a process for making decisions about the parameters and distributions in the general population based on the results from the sample.

The inferential statistics or inductive poses and solves the problem of establishing estimates and conclusions on a population from the results of a sample. Statistical models act bridge between the observed (sample) and unknown (population). The construction and study are based on the calculation of probabilities. Thus, statistical inference methodology is aimed at making descriptions, predictions, comparisons and generalizations of a statistical population from information contained in a sample (Kirk, 2008). Results obtained by using descriptive statistics and rely heavily in the Calculus of Probabilities.

Measures

The main categories of one hundred plied in the procedure of statistical inference are random variables and their theoretical distributions. Random events are those results obtained by the relationship of the process, which may in a particular set of conditions that occur or not occur.

If the realization of a particular experience of each case gives the same event A, the event is called a certain event. However, if the implementation of each case does not experience an event A, event A is a realization we consider to be impossible. If implementation of random events sometimes leads to an event A, and sometimes not, we call it an accidental event. Random variable is a result of the experience takes a specific value for the execution of this experience and not ...