Week 11 Practical Lab worksheet

[Name of the institute]

Week 11 Practical Lab worksheet

The purpose of this paper is to analyze the Mutual Funds data set given for the week 11; in this paper we would be discussing about the descriptive statistics their procedure, implementation and interpretation of the results. Descriptive statistics are commonly encountered, relatively simple, and for the most part easily understood. Most of the statistics encountered in daily life, in newspapers and magazines, in television, radio, and Internet news reports, and so forth, are descriptive in nature rather than inferential. Compared with the logic of inferential statistics, most descriptive statistics are somewhat intuitive. Typically the first five or six chapters of an introductory statistics text consist of descriptive statistics (means, medians, variances, standard deviations, correlation coefficients, etc.), followed in the later chapters by the more complex rationale and methods for statistical inference (probability theory, sampling theory, t and z tests, analysis of variance, etc.)

Although most of the descriptive building blocks of statistics are relatively simple, some descriptive methods are high level and complex. Consider multivariate descriptive methods, that is, statistical methods involving multiple dependent variables, such as factor analysis, principal components analysis, cluster analysis, canonical correlation, or discriminant analysis. Although each represents a fairly high level of quantitative sophistication, each is primarily descriptive. In the hands of a skilled analyst, each can provide invaluable information about the holistic patterns in data. For the most part, each of these high-level multivariate descriptive statistical methods can be matched to a corresponding inferential multivariate statistical method to provide both a description of the data from a sample and inferences to the population; however, only the descriptive methods are discussed here.

The topic of descriptive statistics is therefore a very broad one, ranging from the simple first concepts in statistics to the higher reaches of data structure explored through complex multivariate methods. The Mutual Funds workbook comprise of 9 different variables, four of them are from nominal category (Category, Objective, Fees, and risk), whereas the other 5 variables are scale (Assets, Expense Ratio, Return 2006, 3-Year Return and 5-Year Return). The descriptive statistics in the table below shows the overall picture of the data set; it can be observed that the mean value for the assets is 1726.2, the range i.e. 84800 of the variable shows that there exists a huge difference between the minimum and maximum value. Statistical index obtained by taking the square root of the variance, which describes the variability of a distribution and, therefore, the dispersion of individual scores around a mean value. The standard deviation is zero (s = 0) when all results are identical, its value would be in maximum contrast if the results were distributed to the same extent at both ends of the scale of measurement. The higher magnitude of range leads towards a 5683.51807 units of higher standard deviation.

Descriptive Statistics

N

Range

Minimum

Maximum

Mean

Std. Deviation

Variance

Skewness

Kurtosis

Statistic

Statistic

Statistic

Statistic

Statistic

Std. Error

Statistic

Statistic

Statistic

Std. Error

Statistic

Std. Error

Assets

868

8.48E4

31.50

84842.60

1.7262E3

1.92911E2

5683.51807

3.230E7

9.201

.083

108.250

.166

Expense Ratio

868

3.21

.15

3.36

1.1881

.01305

.38453

.148

.429

.083

2.964

.166

Return2006

868

44.00

-9.00

35.00

12.5142

.21355

6.29158

39.584

-.298

.083

.020

.166

YearReturn_3

868

38.00

-11.90

26.10

10.9930

.13800

4.06566

16.530

-.463

.083

2.045

.166

YearReturn_5

868

43.60

-17.60

26.00

7.7213

.16460

4.84949

23.518

.034

.083

1.507

.166

Valid N (listwise)

868

The magnitudes of skewness and kurtosis for the variable of Assets shows that the data for the particular variable ...