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Research question
Linear regression analysis is used to describe how a dependent variable (sometimes known as the criterion variable) is controlled by an explanatory variable (sometimes known as the predictor variable). The dependent variable Y is plotted against the independent variable X to obtain a scatter plot (Jackson, 2012). Plotting the independent variables against the dependent variables gives a straight-line graph from which a linear regression equation can be obtained (Jackson, 2012). Linear regression tests assume that there is a causal effect on the response variable. However, there are advanced approaches to describe non-dependent relationships (Polit & Lake, 2010). The study will aim at answering this research question:
What is the effect of drinking one soda, two sodas and no soda per day on fifth graders weight gain?
The study will use three independent variables and one dependent variable. The independent variables that will be controlling the dependent variable will be one soda, two sodas and no soda. The dependent variable will be the weight gain of the study participants who will be fifth graders. The study will attempt to associate drinking soda with weight gain. The research question fits to be addressed by the linear regression analysis because it has one dependent variable and three independent variables (Burns & Grove, 2009).
Hypotheses
The proposed study will use the following null hypothesis:
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H0: µweightonesoda = µweighttwosodas = µweightnosoda
Where:
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H0 = null hypothesis
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µweightonesoda = mean increase in weight of fifth-graders drinking one soda daily
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µweighttwosodas = mean increase in weight of fifth-graders drinking two sodas daily, and
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µweightnosoda = mean increase in weight of fifth-graders drinking no soda daily.
The null hypothesis suggests that no significant variations will exist in weight gain in fifth graders drinking one soda, two sodas and no soda daily.
The study will use an alternative hypothesis annotated and stated below:
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H1: µweightonesoda ` µweighttwosodas ` µweightnosoda
Where:
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H1 = alternative hypothesis
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µweightonesoda = mean increase in weight of fifth-graders drinking one soda daily
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µweighttwosodas = mean increase in weight of fifth-graders drinking two sodas daily, and
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µweightnosoda = mean increase in weight of fifth-graders drinking no soda daily.
The alternative hypothesis implies that there will be differences in weight gain in fifth graders drinking one soda, two sodas and no soda daily.
Variables used in the study and expected relationship
The study will assess the relationship between drinking soda and weight gain. Drinking one soda, two sodas and no soda will be the independent variables while weight gain will be the dependent variable. Drinking no soda will be used as the control independent variable. The independent variables will be expected to have effects on the dependent variable. The study will attempt to find a linear regression equation for the three independent variables and the dependent variable. For example, it will examine the effect of drinking one soda on weight gain. It will also determine the effect of drinking two sodas on weight gain. Finally, the study will attempt to determine the relationship between drinking no soda and weight gain in the study participants.
It will be expected that taking two sodas will predict more weight gain than drinking one soda. It will also be expected that drinking no soda will have no significant effect on weight gain. Confirmation of such relationships will lead to rejection of the null hypothesis, but adoption of the alternative hypothesis. The results of the study will be important in the healthcare sector because they will either dispute or support the claim that drinking soda leads to weight gain.
References
Burns, N., & Grove, S. K. (2009). The practice of nursing research: Appraisal, synthesis, and generation of evidence. Philadelphia, PA: Saunders Elsevier.
Jackson, S. L. (2012). Research methods and statistics: A critical thinking approach (4th ed.). Belmont, CA: Wadsworth.
Polit, D. F., & Lake, E. (2010). Statistics and data analysis for nursing research. New York, NY: Pearson.
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