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Introduction to strategy implementation involves translating strategic plans into actions and achieving desired outcomes. While many organizations focus on the technical aspects of implementation, it is crucial to consider the behavioral dimensions as well. Behavioral dimensions encompass the attitudes, behaviors, and actions of individuals within an organization that impact the success of strategy implementation. Here are some key points to consider regarding the behavioral dimensions of strategy implementation:

Organizational culture: The culture of an organization plays a significant role in strategy implementation. It comprises shared values, norms, beliefs, and practices that influence employee behavior. An accommodating and flexible culture fosters employee engagement, commitment, and alignment with strategic goals.

Leadership and Communication: Effective leadership is essential for successful strategy implementation. Leaders must articulate the strategy clearly, provide guidance, and motivate employees towards the desired outcomes. Communication channels ought to be open, transparent, and promote a culture of collaboration and feedback.

Employee Involvement and Empowerment: Involving employees in the strategy implementation process increases their commitment and ownership. Empowering employees through delegation of authority and decision-making cultivates a sense of responsibility and accountability, leading to enhanced performance and innovation.

Introduction

Strategy in the global context requires a thorough understanding of the external environment, including economic, political, cultural, and technological factors that could impact an organization's operations. Organizations must also understand their internal strengths, weaknesses, opportunities, and threats to develop effective strategies that align with their goals. A global strategy involves developing a standardized approach to business activities that can be applied across different countries and cultures, while also adapting to local Market Conditions. Companies that operate globally must consider the differences in legal and regulatory environments, cultural norms, and consumer preferences when developing their strategies. Global strategy requires collaboration across different regions and functions within an organization to achieve a shared vision and common goals. The use of technology and data analytics is crucial in developing and executing global strategies, as it can help organizations identify new market opportunities, monitor changes in the external environment, and make informed decisions. Companies achieving success in formulating and executing global strategies must demonstrate agility and rapid adaptability to external environmental shifts. Additionally, they should maintain a steadfast commitment to continuous improvement and innovation to maintain a competitive edge.

Q.1 If the sampling units of the population are of heterogeneous nature then stratified random sampling scheme is suitable for sample survey to be conducted.

Q.2 In stratatified random sampling scheme, the strata are homogeneous within themselves but heterogeneous between themselves.

3.1 Introduction

Out of all methods of sampling, the most commonly used procedure in surveys is the stratified random sampling. When population units are of heterogeneous nature and if we select a sample using SRSWOR scheme then the sample selected may not be the representative sample and the estimate worked out from that sample will not be the reliable estimate of the population parameter. The estimate of sampling variance of the sample estimator obtained from that sampling scheme may happen to be very high leading to get a less precise estimate of the parameter. In such type of population, to overcome these draw backs, we use another sampling scheme known as stratified random sampling which is defined as follows.

Q.1 Let there be two factors A and B with p and q levels respectively which are applied to the larger strip by dividing the experimental material into p rows and q columns. The factors A and B are applied at random to these rows and columns respectively. Such a layout is known as strip plot design.

CHI- SQUARE TEST (χ₂ TEST)

The chi square test is used to determine if the two attributes are independent of each other. Chi square is a measure to evaluate the difference between observed frequencies and expected frequencies to examine whether the difference so obtained is due to a chance factor or due to sampling error.

In a contingency table if each attribute is divided into two classes it is known as 2×2 contingency table. When one attribute is divided into two classes and another into r or c resultant contingency table is known as r×2 or 2×c contingency table. Here, r denotes the number of rows and c the number of columns. For such data, the statistical hypothesis under test is that the two attribute are independent of one another. To test this hypothesis we use the test statistic

FOR GROUPED DATA

Objective: Data regarding lives of two new models of refrigerators collected in a recent survey are

COEFFICIENT OF VARIATION:

The most important of all the relative measure of dispersion is the coefficient of variation. This word is variation not variance. There is no such thing as coefficient of variance. The coefficient of variation( C.V.) is defined

CORRELATION

In case of bivariate or multivariate normal distributions, we may be interested in discovering and measuring the magnitude and direction of the relationship between two or more variables. For this purpose we use correlation. The sys- tematic interrelationship between the variables is termed as correlation. When only two variables are involved the correlation is known as simple correlation. If more than two variables are involved the correlation is known as multiple correlation. An increase in one variable may cause an increase in the other variable, or a decrease in one variable may cause a decrease in the other vari- able, then they are said to be positively correlated. If a decrease in one variable causes an increase in the other variable or vise versa, the variables are said to be negatively correlated.

F TEST (VARIANCE RATIO TEST)

F distribution is applied in several tests of significance relating to the equality of two sampling variances drawn on the basis of independent samples from a normal population.

KURTOSIS

Kurtosis refers to the extent to which unimodal frequency curve is peaked. Kurtosis is a measure that refers to the peakedness of the top of the curve. Kurtosis gives the degree of flatness in the region about the mode of a frequency distribution.

Types of Kurtosis

There are three categories of kurtosis that can be displayed by a set of data. All measures of kurtosis are compared against a standard normal distribution, or bell curve.

The first category of kurtosis is a mesokurtic distribution. This type of kurtosis is the most similar to a standard normal distribution in that it also resembles a bell curve. However, a graph that is mesokurtic has fatter tails than a standard normal distribution and has a slightly lower peak. This type of kurtosis is considered normally distributed but is not a standard normal distribution.

The second category is a leptokurtic distribution. Any distribution that is leptokurtic displays greater kurtosis than a mesokurtic distribution. Characteristic of this type of distribution is one with extremely thick tails and a very thin and tall peak. The prefix of “lepto-” means “skinny,” making the shape of a leptokurtic distribution easier to remember. T-distributions are leptokurtic.

The final type of distribution is a platykurtic distribution. These types of distributions have slender tails and a peak that’s smaller than a mesokurtic distribution. The prefix of “platy-” means “broad,” and it is meant to describe a short and broad-looking peak. Uniform distributions are platykurtic.

COEFFICIENT OF THE MEAN DEVIATION

A relative measure of dispersion based on the mean deviation is called the coefficient of the mean deviation or the coefficient of dispersion. It is defined as the ratio of the mean deviation to the average used in the calculation of the mean deviation.

MEAN DEVIATION

The mean deviation is the first measure of dispersion that we will use that ac- tually uses each data value in its computation. It is the mean of the distances between each value and the mean. It gives us an idea of how spread out from the center the set of values is.

The mean deviation is based on all the observations, a property which is not possessed by the range and the quartile deviation. The formula of the mean de- viation gives a mathematical impression that is a better way of measuring the variation in the data. Any suitable average among the mean, median or mode can be used in its calculation but the value of the mean deviation is minimum if the deviations are taken from the median.

Objective: The distribution of age of males at the time of marriage was as follows

Measures of Central Tendency

The term ‘Mean” or ‘average’ is something, we have been familiar with from a very early age when we start analyzing our marks on report card. We add together all of our test results and then divide it by the sum of the total number of marks there are. We often call it the average. However, statistically it’s the Mean!

The Median is the ‘middle value’. When the totals of the list are odd, the me- dian is the middle entry in the list after sorting the list into increasing order. When the totals of the list are even, the median is equal to the sum of the two middle (after sorting the list into increasing order) numbers divided by two. Thus, remember to line up values, the middle number is the median!

The Mode in a list of numbers refers to the list of numbers that occur most frequently. A trick to remember this one is to remember that mode starts with the same first two letters that most does. Most frequently - Mode.

MOMENTS

The term moment in mechanics refers to the measure of a force with reference to its tendency to produce rotation. The strength of this tendency is depen- dent upon the amount of the force and the distance from the origin at which the force is applied. If on both the sides of the origin the force are equal than there will be a balance, at a balanced position the positive product equals the negative product.

Moment is used in Statistics in a quite analogous sense. The class frequencies are looked upon as the forces and the deviations of the different values from the mean are taken as the distances. In other words, moment is the mean of the first, second, third, fourth, etc. powers of deviations of the different values from the arithmetic mean. The first moment about the mean is always zero and second moment about the mean is variance.

If the moment calculated from arithmetic mean are called central moments and denoted by μ with a subscript which indicate the order of the moment. Moments are the mean values of powers of the deviations in any frequency distribution taken about three points

ANALYSIS OF VARIANCE

Analysis of Variance (ANOVA) is a hypothesis-testing technique used to test the equality of two or more population (or treatment) means by examining the variances of samples that are taken. ANOVA allows one to determine whether the differences between the samples are simply due to random error (sampling errors) or whether there are systematic treatment effects that cause the mean in one group to differ from the mean in another.

Most of the time ANOVA is used to compare the equality of three or more means, however when the means from two samples are compared using ANOVA it is equivalent to using a t-test to compare the means of independent samples.

ANOVA is based on comparing the variance (or variation) between the data samples to variation within each particular sample. If the between variation is much larger than the within variation, the means of different samples will not be equal. If the between and within variations are approximately the same size, then there will be no significant difference between sample means.

A relative measure of dispersion based on the quartile deviation is called the coefficient of quartile deviation. It is defined as

Paratition Value

Median is calculated for dividing the series into two equal parts. If the series is divided into four, eight, ten or hundred parts then the measures are called Quartile, Octile, Decile and Percentile respectively denoted by Q_(1,2,3), O_(1,2..7), D_(1,2…9) and P_(1,2…99).

REGRESSION

Regression means to revert or return back. The term was first introduced by Sir Francis Galton in1877. Regression technique is applicable in all those fields where two or more relative variables have the tendency to go back to the mean. Regression analysis refers to the methods by which estimates are made of the values of a variable from the knowledge of the values of one or more other variables and to the measurement of the errors involved in this estimation pro- cess.

In statistical modeling, regression analysis is a statistical process for estimat- ing the relationships among variables. It includes many techniques for mod- eling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. Regres- sion analysis helps one understand how the typical value of the dependent variable (or ‘criterion variable’) changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed.