A Multiple Regression Analysis Of Factors That Affect Millet Yield Using SPSS Statistical Package

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A Multiple Regression Analysis Of Factors That Affect Millet Yield Using SPSS Statistical Package


Abstract


The study aims at examining the use and miss-use of the Internet by secondary school students in Nigeria. This study covers all regular secondary students of the Secondary school of Abuja, Nigeria. The population for this study is the entire secondary students of the Kogi State Secondary school including 500 level law students which brings the population to fifteen thousand (15,000) students. A survey research was adopted to meet the objectives of the study. With the aid of random sampling, 375 sampled students were administered questionnaires in which only 200 were dully answered and returned. The results have so far demonstrated that the use of internet contributes immensely to the academic performances of students. The major findings include slow functioning of internet servers, lack of computer skills among secondary students and problem of paying for online services. There is a need for extensive training programmes organised at regular intervals so that all categories of users can improve their efficiency in use of the internet.


Chapter One


Introduction

1.0 Background of Study

In Africa, Millet is primarily grown for human consumption serving as the staple food in some of the poorest countries and regions of the continent the grain is mainly used in three different ways as a grain-like flour (couscous), as a dough and as a gruel (Brunken et al.,1977; counting and Harris,1968).

Despite the enormous use to which millet can be put, there are some constraints which limit the production of the crop in savannah environment of Northern Nigeria. Among these are nature of soils, climate of the region and cultural techniques and management practise. Other is disease pests, weed and other parasites whose effect seriously affect the yield of the millet.

Millet is believed to have descended from the west African wild grass which was domesticated more than 40,000 years ago (National Research Council, 1996) it spread from west African to East Africa and then to India.

Areas planted with millet are estimated at 15 million hectares annually in Africa and 14 million hectares in Asia. Global production exceeds 10million tone a year (national research council, 1966). The food value of millet is high. Trial in India has shown that millet is nutritionally more superior to maize and rice for human growth. The protein content of millet is higher than maize and has a relatively high vitamin A content (Gallagher 1984).


1.1 Aims and Objective of the Study

This study aims at relating the effectiveness components of millet to millet yield. The parameter include, Establishment score count at two weeks (ESTAB), days of 50% flowering after sowing/germination (DHF), Plant height (PLHT),Pinnacle length (PNCL), 100 seed weigh(HSN) .

Using multiple regression analysis in a bid to determine:

  1. Which factors have a statistically significant effect on the yield of millet.
  2. To build a regression model of yield on the identified factors in the experiment.
  3. To possibly recommend for a further experiment on other factor that influence the yield of millet that are not included in these experiment.

1.2 Significance of Study

After the factors have been identified the researcher will improve to the improvement of millet yield when the factors that influence it are identified.


1.3 Limitation and Scope of Study

The result and recommendation from this project apply to the existing commercially available hybrid verity of millet.

And all the data used and all the observation made are limited to Lake Chad Research Institute., Federal Ministry of Agriculture and Natural Resources.

1.4 Source of Data Collection

Data were collected on a number of parameters, these include seedling Establishment, days of 50% flowering, Plant height, Pinnacle length, 100 seed weight, Biomass, Grain yield. All these were obtained from the Lake Chad Research Institute, federal Ministry of Agriculture and Natural Resources.


1.5 Definition of Terms

ESTAB:

Establishment score count at two weeks

DHF:

Day of 50% flowering after sowing / germination

PLHT:

Plant height

PNCL:

Pinnacle Length

HSN:

100 seed weight

YIELD:

Yield component of millet


1.6 Methodology

The analytical part of work will be done using multiple regression analysis, correlation analysis, regression coefficient and coefficient of determinant through the aid of SPSS Statistical package.

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Conclusion


The multiple regression analysis was used in finding out factors that affect millet yield using SPSS statistical package. The stepwise regression analysis identified only two among the independent variable which are ESTAB (Establishment score count at two weeks) and DHF (Day of 50%flowering after sowing) affect the millet yield.

The regression coefficient for these two variables ESTAB (Establishment score count at two weeks) and DHF (Day of 50%flowering after sowing) are positive. That is unit increase in ESTAB and DHF associate with increase in yield of millet.


Recommendation


From the analysis it is recommended that to bring about increase in yield of millet the research effort should be directed on how to improve ESTAB (Establishment score count at two weeks) and DHF (Day of 50%flowering after sowing) .

Since the R2 suggest a high unexplained variation research effort can be directed towards finding other variable that can improve millet yield.

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