Question

Nonparametric methods; analysis of ordinal data

Index numbers

Time Series and Forecasting

Concept of moving average and its use

Answer 1

Nonparametric Method - A method commonly used in statistics to model and analyze ordinal or nominal data with small sample sizes. Nonparametric models do not require to make any assumptions about the distribution of the population, and so are sometimes referred to as a distribution-free method. Typically, this method will be used when the data has an unknown distribution, is non-normal, or has a sample size so small that the central limit theorem can't be applied to assume the distribution.

Ordinal data - is a categorical, statistical data type where the variables have natural, ordered categories and the distances between the categories is not known. These data exist on an ordinal scale. The ordinal scale is distinguished from the nominal scale by having ordered categories. It also differs from interval and ratio scales by not having category widths that represent equal increments of the underlying attribute.

Index number - is an economic data figure reflecting price or quantity compared with a standard or base value. The base usually equals 100 and the index number is usually expressed as 100 times the ratio to the base value. For example, if a commodity costs twice as much in 1970 as it did in 1960, its index number would be 200 relative to 1960. Index numbers are used especially to compare business activity, the cost of living, and employment. They enable economists to reduce unwieldy business data into easily understood terms.

Time Series and Forecasting - A time series is a set of ordered observations on a quantitative characteristic of a phenomenon at equally spaced time points. One of the main goals of time series analysis is to forecast future values of the series.

Moving average is an indicator in technical analysis that helps smooth out price action by filtering out the “noise” from random price fluctuations. It is a trend-following, or lagging, indicator because it is based on past prices. The two basic and commonly used moving averages are the simple moving average, which is the simple average of a security over a defined number of time periods, and the exponential moving average, which gives greater weight to more recent prices. The most common applications of moving averages are to identify the trend direction and to determine support and resistance levels.