Xacc/280 Week 8 Final Project

The ordinary least squares (OLS) works in regression by computing the values of intercept and slope that represents the best fit for the observation. The regression line in Minitab is based on residuals and or errors by calculating our parameter by minimizing the sum of squares for all the observation errors. In addition, OLS works by finding the best fitting line relationship between variables. By deriving residuals, we minimize the sum of square from the regression line. After that, we will interpret the data of the coefficient, T-Value, and P-Value.

Figure 1 Concentration of glucose relative to elution volume. Graph plotted using Excel. The equation of the line is represented by a 6th order polynomial (y = -4E-08x6 + 8E-06x5 - 0.0006x4 + 0.0184x3 - 0.2952x2 + 2.0705x - 4.6828) with a regression R² = 0.75191.

4) Use cubic regression to determine an equation for the data (or lwh where (12 – x) represents the sides and (x) represents the height of the box).

Linear regression is an approach for modeling the relationship between a scalar dependent variable Y and explanatory variables (or independent variables) denoted X. Function $f(X,W)=Y$ (shown below) can be learned to predict future values.

72 hour post discharge phone call from respiratory therapy to reinforce education and to determine if the patient is improving.

BMR= 66+ (13.7 x weight in kg) + (5 x height in cm) – (6.8 x age in years) for male

Pulmonary or respiratory rehabilitation has been proven to help increase chronic obstructive pulmonary disease (COPD) sufferers’ quality of life (QOL), decrease hospitalizations and overall cost of care (Soysa, Mckeough, Spencer & Alison, 2012). Although pulmonary rehabilitation (PR) has shown to have positive benefits, the Committee on National Surveillance System for Cardiovascular and Select Chronic Diseases, Institute of Medicine indicates that less than 2% of COPD patients have participated in any type of PR program (2011, p. 39). Some noted system barriers include physician referral patterns that range between 3% and 16% internationally and lack of program availability (Johnston & Grimmer-Somers, 2010). Patient barriers also exist

The Biodex Gait Trainer 2TM used to evaluate gait parameters including; average step length (m), walking speed (m/sec), and time on each foot (recorded as a percent of gait cycle). For evaluation of gait parameters, each child first allowed to be familiar with the gait trainer set up before starting recording the gait parameters. This achieved through instructing the child to walk over the gait trainer and to follow the tread belt movement for 3-5 minutes. This might be repeated two or three times till the child became adapted with the apparatus.

In Six Minute Walk Test my client was able to walk 2250 feet and had a predicted Vo2 max of 45.9 mL of O2/Kg. In the One Mile Walk Test my client finish in 17.12 minutes and a heart rate of 165. This gave him a predicted Vo2 max of 35.38. For the Bruce Submaximal Treadmill Test.

The coefficient of -0.05 represents the rate at which Ryan’s two-mile run time is decreasing. However, the term “13.75” represents the two-mile running time Ryan started with. If you substitute the value ten for w, you could find out the speed that Ryan runs two-miles at the end of two weeks.

So you first predict the independent variable, then look at the established relationships between that independent variable and the dependent ones to predict what the dependent variables will be. You then develop an equation that summarizes the effects of predictor variables.

where Y_i is the response variable, Χ_i is a regressor variable, β_0 and β_1 are unknown parameters to be estimated, and ε_i is an error term. This model is termed the simple linear regression model, because it is linear in β_0 and β_1 and contains only a single regressor

Biostatistics is important to study as undergraduates delve deeper into their studies of Biology and learn how the study of life is integrated into more than just their college-level science courses. Looking into the use of statistics at a scientific level at this stage of our education is preparing determined and enthusiastic students for the world of medicine as one day we will have to read and analyze sets of data and more than likely give the statistics of our patients’ issues. Biostatistics is allowing students to explore the world of medicine using a different approach, mathematically and critically. The purpose of this experiment was to determine if a significant multiple regression exists between my 3 quantitative variables, Weight, Height and Age and to determine the best regression model to use when making predictions.

The least square technique based on linear, exponential, asymptotic, curvilinear and logarithmic equations has been applied on the available data to produce the estimated data. The error analysis has been made to produce estimated error. It has been observed that average error based on least square technique based on linear equation has shown the minimum error (2.25%) as compared to the other models according to table 2. Therefore least square technique based linear equation has been chosen as the best known solution.

Regression analysis involves finding a relationship between a response variable and a number of explanatory variables. For a sample number t, with p explanatory